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Question 81:
A multinational intelligent agriculture technology corporation uses Dynamics 365 Customer Insights to unify data from smart irrigation systems, drone crop imaging analytics, fertilizer subscription purchases, agricultural consultant field logs, mobile farm management apps, equipment telemetry, and sustainability program participation. They want to calculate a “Farm Innovation Utilization Score” based on recency of app activity, frequency of drone imaging requests, adoption of smart irrigation automation, volume of connected equipment telemetry packets, agricultural consultant follow-up interactions, and fertilizer subscription renewal consistency over the last 200 days. This score must refresh automatically and appear as a numeric profile attribute for segmentation and predictive yield improvement journeys. Which Customer Insights capability should they use to compute this score?
A) Create a segment based on innovation utilization
B) Build a measure that aggregates innovation-related behaviors
C) Use deterministic matching to analyze farm innovation usage
D) Import the innovation score manually every quarter
Answer: B
Explanation:
The scenario describes a complex agricultural ecosystem where multiple data streams converge into Customer Insights. Farmers interact with drone imaging, smart irrigation equipment, fertilizer programs, consultant logs, and mobile farm management applications. The company wants to compute a Farm Innovation Utilization Score to measure how frequently and effectively growers use advanced technologies. Because this score must combine several types of interaction data and update automatically as new data arrives, measures in Customer Insights are the appropriate tool.
Measures allow organizations to create numerical values based on aggregated interaction data from multiple external sources. They support mathematical weighting, time-window filtering, summations, normalization, comparisons, and advanced calculations. In this agricultural scenario, each input—drone imaging frequency, app recency, telemetry volume, irrigation automation events, fertilizer subscription consistency—can be built into a mathematical scoring model that Customer Insights continuously computes.
Drone imaging frequency is a significant indicator of innovation adoption. Farmers who schedule drone imaging to analyze soil health, crop vitality, and irrigation needs demonstrate proactive engagement with modern precision agriculture. Measures can count drone imaging events and assign weights.
Smart irrigation adoption is another critical factor. Modern irrigation systems automatically adjust watering cycles using soil moisture sensors, weather predictions, and crop needs. If a farm enables irrigation automation and interacts with its controls frequently, this reflects higher innovation usage. Measures can calculate how many automation triggers occurred over the last 200 days.
Agricultural consultant follow-up interactions represent another behavioral indicator. When farmers regularly engage with consultants, they demonstrate proactive management practices. Measures allow these interactions to be incorporated.
Option A is incorrect because segmentation is not able to calculate a numeric score. Segmentation can group farmers into categories once the score exists, but segmentation cannot compute a weighted behavioral measure.
Option C is incorrect because deterministic matching has nothing to do with computing engagement. Matching simply identifies duplicate profiles.
Option D is incorrect because manually importing quarterly scores undermines the required automatic refresh. Drone imaging, telemetry, and consultant logs update daily. Waiting months for refresh cycles would lead to inaccurate agricultural insights.
Measures are the only method that can combine all behavioral components in a dynamic calculation, making option B correct.
Question 82:
A global smart mobility and public transportation operator uses Dynamics 365 Customer Insights to unify commuter data from metro card tap-ins, bus ticketing systems, e-bike sharing logs, rideshare partner interactions, mobile journey-planning app behavior, and customer service feedback. The operator also maintains an external machine learning model that produces a monthly “Commuter Service Optimization Probability Score” for each passenger, estimating how likely that commuter is to shift to premium mobility packages. They want this score to be imported, mapped to unified profiles, and automatically refreshed every month. Which Customer Insights integration method should they use?
A) Manually input the optimization score for each commuter
B) Import the ML dataset as an enrichment table and map it to unified profiles
C) Use segmentation to estimate probability scores
D) Use deterministic matching to generate the probability score
Answer: B
Explanation:
This scenario describes a metropolitan mobility operator that wants to incorporate monthly machine learning–generated probability scores into Customer Insights. These scores predict whether commuters will shift to premium service tiers. Because the probability values originate from an external analytics system, Customer Insights must ingest and map them as enrichment data, making enrichment tables the correct solution.
Enrichment tables allow organizations to upload external datasets on a scheduled basis and map values to unified profiles. Once ingested, Customer Insights treats these values as native profile attributes. The commuting operator can then use these attributes to perform segmentation, personalize offers, create journeys, or analyze conversion trends.
Option A fails because manually entering scores is impossible for the large number of commuters using public transportation systems. Metro operators may serve millions of passengers across bus, subway, and shared mobility ecosystems. Manual entry would be massively inefficient and error-prone.
Option C is incorrect because segmentation cannot compute or approximate predictive probability values. Segmentation simply evaluates existing conditions. A segment can classify commuters with probability above 0.75, for instance, but segmentation cannot produce the probability.
Option D is incorrect because deterministic matching is designed solely to identify and merge duplicated commuter profiles across systems. It does not compute probability scores or interpret behavioral patterns.
Enrichment tables solve all the challenges. The ML engine outputs a file each month containing commuter identifiers and probability scores. Customer Insights ingests the data, maps it to profiles, and refreshes the attributes automatically. This allows transit operators to create segments such as commuters with high probability scores but low mobile app engagement, or commuters with medium scores who consistently use e-bike sharing services.
Because enrichment tables support automated ingestion and mapping of predictive datasets, option B is correct.
Question 83:
A global insurance and risk management company uses Dynamics 365 Customer Insights to unify policyholder profiles from claims submissions, mobile policy apps, agent interactions, call center records, risk assessment surveys, roadside assistance logs, and IoT-based driving behavior telemetry. They want to compute a “Behavioral Risk Minimization Score” that evaluates policyholder engagement in low-risk driving habits based on telemetry stability, frequency of safe-driving rewards participation, recency of risk education content engagement, volume of mobile app safety interactions, and historical claim-free intervals over the last 365 days. This score must refresh automatically and appear as a numeric profile attribute. Which Customer Insights capability should they use to compute this score?
A) Create a segment that groups policyholders by risk level
B) Use a measure to calculate the risk minimization score
C) Rely on deterministic matching to identify low-risk drivers
D) Manually import the score yearly
Answer: B
Explanation:
This scenario involves a large insurance company wanting to compute a behavioral risk minimization score using multiple policyholder behaviors. Because the model involves aggregating telemetry, digital engagement, educational participation, claim histories, and reward activities, measures are the correct tool.
The Behavioral Risk Minimization Score must incorporate numerous signals. Telemetry stability is one of the strongest indicators. Policyholders whose IoT driving device sends frequent, consistent, and stable readings are often safer drivers. Measures can compute telemetry-based metrics such as average acceleration variance or frequency of harsh braking incidents.
Participation in safe-driving reward programs is another indicator. Policyholders who regularly engage with these programs demonstrate a desire to lower risk. Measures can count reward interactions within the last year.
Recency of safety education content engagement also matters. Policyholders reading risk education articles or watching safety videos typically exhibit proactive behavior. Measures can incorporate recency filters to ensure old engagement does not inflate the score.
Mobile app interactions also influence risk minimization. Policyholders who frequently use safety tools within the app, such as trip analysis or risk calculators, may demonstrate active risk management.
Historical claim-free intervals strongly correlate with low-risk behavior. Measures allow the company to incorporate the number of days since last claim or count claim-free years.
Option A is incorrect because segmentation only groups users based on existing attributes; it cannot compute new numerical scores.
Option C is incorrect because deterministic matching only resolves duplicate records.
Option D is incorrect because manually importing scores annually will not reflect dynamic behavior patterns over the year.
Because measures can aggregate, calculate, and refresh behavioral scores automatically, option B is correct.
Question 84:
A global entertainment theme park corporation uses Dynamics 365 Customer Insights to unify visitor data from ticket purchases, ride access wristband scans, mobile app interactions, dining reservations, in-park purchases, loyalty memberships, and post-visit feedback surveys. They want to identify visitors with high “Attraction Experience Excitement Scores” from their internal analytics system but who have shown no mobile app activity, no ride planning interactions, and no dining reservations in the last 40 days. They want to target these visitors with early-access promotions and personalized return-visit packages. Which segmentation approach should they use?
A) Create a segment filtered only by excitement score
B) Create a segment filtered only by inactivity
C) Combine excitement score and recency inactivity into one segment
D) Use deterministic matching to identify inactive high-excitement visitors
Answer: C
Explanation:
This scenario requires the theme park to identify two behavior patterns simultaneously: high excitement scores and recency-based inactivity. The Attraction Experience Excitement Score is likely calculated externally and imported into Customer Insights. However, creating the target audience requires combining that score with multiple inactivity filters. Customer Insights segmentation supports multi-condition rules, making option C the correct solution.
Option A is insufficient because excitement alone does not indicate disengagement. Some high-excitement visitors may still be using the mobile app daily.
Option B is insufficient because inactivity alone will include visitors without elevated excitement scores, making the segment too broad.
Option D is incorrect because deterministic matching does not analyze excitement or activity patterns.
Option C allows the company to specify conditions such as:
Attraction Excitement Score above a certain threshold
AND
No mobile app interactions in 40 days
AND
No dining reservations in 40 days
AND
No ride planning interactions in 40 days
This identifies visitors who previously showed strong excitement but have stopped planning visits, making them ideal targets for personalized re-engagement.
Thus, option C is correct.
Question 85:
A multinational luxury automotive manufacturer uses Dynamics 365 Customer Insights to unify data from test drive registrations, dealership CRM logs, vehicle configuration tools, mobile car companion app usage, maintenance histories, connected vehicle telemetry, and loyalty memberships. They operate an external predictive engine that produces a monthly “Premium Upgrade Likelihood Score” for each owner. They want this score imported into Customer Insights, mapped to unified profiles, and automatically refreshed each month. Which Customer Insights integration method should they use?
A) Manually input the likelihood score
B) Import the predictive file as an enrichment table and map it to unified profiles
C) Use segmentation to estimate upgrade likelihood
D) Use deterministic matching to calculate the probability
Answer: B
Explanation:
This scenario describes the need to import monthly predictive analytics into Customer Insights. The Premium Upgrade Likelihood Score comes from an external predictive engine, so Customer Insights must ingest this dataset using enrichment tables. Enrichment tables support automated ingestion, mapping, and profile attribute integration.
Option A is impractical because manually entering scores each month for thousands of luxury car owners is inefficient and error-prone.
Option C is incorrect because segmentation cannot compute predictive values. It only classifies customers using existing data.
Option D is incorrect because deterministic matching only resolves duplicate records and does not generate predictive scores.
Enrichment tables allow the company to map the externally generated score into unified profiles and refresh it automatically each month, enabling segmentation such as identifying high-likelihood owners who have not yet configured new models.
Thus, option B is correct.
Question 86:
A multinational aviation services corporation uses Dynamics 365 Customer Insights to unify customer data from flight booking platforms, airport lounge access systems, frequent flyer programs, mobile check-in activities, in-flight Wi-Fi usage logs, cabin crew service feedback, partner hotel stays, and travel insurance purchases. They want to compute a “Premium Traveler Engagement Momentum Score” that reflects how quickly a traveler’s engagement is increasing or decreasing over the last 120 days. This score must consider recency and frequency of lounge visits, upgrade purchases, in-flight digital service usage, loyalty tier progression, partner activity volume, and behavioral stability across multiple travel cycles. They want the score dynamically recalculated and added to unified profiles for segmentation, loyalty retention journeys, and elite experience targeting. Which Customer Insights capability should they use to compute this score?
A) Create a segment to classify premium travelers by engagement
B) Build a measure that aggregates premium traveler engagement momentum
C) Use deterministic matching to detect travelers with rising engagement
D) Import the score manually after quarterly analysis
Answer: B
Explanation:
This aviation corporation scenario is a textbook example of when Customer Insights measures provide the exact capabilities required to compute a dynamic behavioral score. The company wants to calculate engagement momentum—essentially the rate at which a traveler’s activity, loyalty behavior, and premium-service interactions are trending upward or downward over the past 120 days. Because the score requires aggregating multiple data types, incorporating recency filters, applying weighted logic, evaluating trends, and supporting automated refreshes,
In-flight Wi-Fi usage logs provide additional insight. High-volume usage of digital services can indicate strong engagement with value-added in-flight products. Behavioral changes in Wi-Fi usage—such as rising connectivity sessions—can also signal momentum. Measures allow aggregation of recent usage and trend comparison.
Option A is incorrect because segmentation cannot compute numeric trends or aggregate multi-source interactions. Segments classify travelers based on existing profile attributes or measure outputs but cannot compute engagement momentum themselves.
Option C is incorrect because deterministic matching only merges duplicate traveler records using identifiers like passport numbers, loyalty IDs, or email addresses. Matching has no analytical ability and cannot evaluate momentum or engagement rates.
Option D is incorrect because manual imports violate the requirement for automatic recalculation. Traveler activity changes daily: lounge visits, flight check-ins, Wi-Fi sessions, partner purchases, and loyalty point accruals are constant. Manual quarterly updates would be outdated and useless for high-value personalization.
Measures uniquely support:
Time-bounded calculations (ex: last 120 days)
Aggregation of multi-source interactions
Weighted scoring formulas
Trend evaluation
Automatic recalculation when new data arrives
Integration into segmentation and journeys
This makes measures the only suitable feature for computing the Premium Traveler Engagement Momentum Score.
Thus, the correct answer is option B.
Question 87:
A global pharmaceutical research organization uses Dynamics 365 Customer Insights to unify data from patient education portals, clinical trial participation logs, prescription adherence tracking, physician appointment schedules, wearable health device telemetry, wellness program engagement, and virtual care consultations. They want to create a segment identifying patients with high “Health Adherence Stability Scores” but who have shown no portal logins, no wellness program interactions, and no wearable device sync events in the last 75 days. The organization wants to target these patients with educational re-engagement and adherence reinforcement journeys. Which segmentation approach should they use?
A) Create a segment filtered only by adherence score
B) Create a segment filtered only by inactivity across digital channels
C) Combine adherence score and inactivity rules into a single segment
D) Use deterministic matching to detect digitally inactive patients
Answer: C
Explanation:
This scenario requires combining two distinct analytical criteria to build a highly targeted segment. The organization wants to identify patients who have high Health Adherence Stability Scores but who have stopped interacting with digital platforms such as patient portals, wearable sync systems, and wellness programs. The only Customer Insights capability that supports combining these conditions into a single, automatically updating audience is segmentation using multi-condition rules.
The first part of the requirement is the Health Adherence Stability Score. This score, computed either through a measure or imported from an external analytics engine, likely evaluates long-term behavioral consistency such as medication adherence, appointment attendance reliability, symptom tracking frequency, and wearable telemetry regularity. High scores suggest that patients historically follow their prescribed care plans well.
The second requirement is inactivity across multiple channels:
No patient portal logins in 75 days
No wellness program engagement in 75 days
No wearable sync events in 75 days
These digital behaviors are essential indicators. When they decline across all channels simultaneously, it may indicate that the patient is drifting away from routine engagement. This decline may precede worsening adherence, increased risk, or reduced wellness outcomes.
Option A fails because filtering only by adherence score will include patients who are deeply engaged digitally and who do not need re-engagement interventions.
Option B fails because filtering only by inactivity would include patients with historically poor adherence stability. These patients may require a different intervention strategy, such as clinical escalation rather than educational reinforcement.
Option D is incorrect because deterministic matching is a tool for unifying duplicate profiles, not identifying behavioral or digital engagement patterns.
Option C is correct because segmentation can combine multiple filters:
Adherence Stability Score ≥ defined threshold
AND
No portal login in 75 days
AND
No wellness activity in 75 days
AND
No wearable sync events in 75 days
Segments in Customer Insights refresh automatically as new behaviors occur. If a patient logs into the portal tomorrow or syncs their wearable device, they automatically exit the segment. This ensures precision targeting.
Therefore, option C is correct.
Question 88:
A multinational financial wellness and investment advisory company uses Dynamics 365 Customer Insights to unify data from retirement planning tools, investment portfolio dashboards, financial counselor sessions, digital budgeting apps, credit score monitoring activity, customer support logs, and tax optimization services. They operate an external predictive model that generates a quarterly “Financial Goal Achievement Probability Score” estimating how likely each customer is to meet their financial targets in the next 12 months. They want this score imported into Customer Insights, mapped to unified profiles, and updated automatically each quarter. Which Customer Insights integration method should they use?
A) Manually input each customer’s probability score
B) Import the predictive model output as an enrichment table
C) Use segmentation to approximate probability outcomes
D) Use deterministic matching to compute probability values
Answer: B
Explanation:
This scenario involves integrating external predictive insights into Customer Insights in a recurring, automated fashion. The Financial Goal Achievement Probability Score is generated quarterly by a machine learning model that analyzes retirement savings behaviors, investment diversification, risk tolerance, budgeting patterns, credit stability, and overall financial wellness indicators. The company wants this probability score added to each unified profile and refreshed every quarter.
Enrichment tables are the only Customer Insights capability designed for importing third-party predictive model output and mapping it to unified profiles. Enrichment ingestion supports scheduled updates, ensuring the probability scores are refreshed quarterly when the predictive model is rerun.
Option A is incorrect because manually entering scores is unmanageable for a large customer base and contradicts the requirement for recurring automated updates.
Option C is incorrect because segmentation cannot compute predictive probability values. Segmentation only groups customers using existing data; it cannot generate probabilities or approximate model output.
Option D is incorrect because deterministic matching only resolves duplicate profile records using identifiers like customer IDs, phone numbers, or email addresses. Matching cannot compute or infer probability values.
Enrichment tables allow the organization to upload each quarterly prediction file into Customer Insights. The file can be mapped to unified profiles using customer ID or account number. Once imported, the probability score becomes a standard profile attribute. It can then be used for:
Segmentation
Personalized financial wellness journeys
Proactive counselor outreach
Automated retirement planning recommendations
Risk-adjusted investment suggestions
Because enrichment tables support automated ingestion, scheduled updates, and seamless profile mapping, option B is the correct answer.
Question 89:
A global green-energy and sustainability consulting firm uses Dynamics 365 Customer Insights to unify account and contact data from carbon footprint auditing tools, sustainability certification programs, IoT-connected energy monitoring devices, environmental compliance submissions, training module completions, mobile sustainability app usage, and partner consultancy interactions. Their internal analytics team generates a monthly “Sustainability Engagement Performance Score” for each corporate client, based on energy reduction trends, compliance adherence patterns, sustainability module recency, IoT sensor telemetry stability, and corporate participation in carbon offset initiatives. They want this score dynamically imported every month, mapped to the correct unified profiles, and available for segmentation, sustainability improvement journeys, and predictive modeling. Which Customer Insights integration method should the firm use?
A) Manually enter each client’s performance score monthly
B) Import the analytics dataset as an enrichment table and map it to unified profiles
C) Use segmentation to estimate sustainability performance
D) Use deterministic matching to generate sustainability scores
Answer: B
Explanation:
This scenario describes a firm that operates multiple sustainability and environmental-impact platforms, each producing complex behavioral and environmental data. Their internal analytics team generates a monthly Sustainability Engagement Performance Score using inputs from carbon reduction tools, IoT sensors, compliance reports, and sustainability training platforms. Because the score is generated externally and must be updated regularly, the correct Customer Insights capability is enrichment tables.
Environmental compliance submissions, including governmental sustainability reporting, contribute to the predictive scoring model. Companies with strong compliance behaviors typically score higher. These compliance datasets are external inputs to the analytics team’s score.
Option A is incorrect because manual entry would undermine timeliness, accuracy, and operational feasibility. Large consulting firms often support hundreds or thousands of corporate clients. Entering scores manually each month would introduce errors and slow the refresh cycle. Customer Insights supports automated ingestion, which is necessary.
Option C is incorrect because segmentation cannot compute predictive or behavioral scores. Segments merely classify profiles into groups based on attributes that already exist. They cannot generate numeric performance values or interpret IoT telemetry stability.
Option D is incorrect because deterministic matching is a record-unification feature. While essential for cleaning up duplicate profiles, it cannot analyze data patterns or compute sustainability performance. It simply identifies matching IDs across systems.
Enrichment tables allow the firm to upload the analytics team’s monthly dataset. Using identifiers like client account number, sustainability program ID, or organizational email domain, Customer Insights maps the records to unified profiles. Once mapped, the Sustainability Engagement Performance Score becomes a usable attribute within the system.
Once the score is in Customer Insights, downstream actions become possible. For example, segments can be built to identify clients with declining performance trends, clients with high performance but low mobile app engagement, or clients with rising performance who are strong candidates for premium consulting packages. Customer journeys can be configured to send automated sustainability improvement recommendations based on the score. Predictive models can incorporate the score as an input for forecasting long-term energy reduction commitments or certification renewals.
Because enrichment tables are the only method capable of reliably ingesting, mapping, and refreshing external predictive or analytical scores, option B is correct.
Question 90:
A multinational supply chain logistics provider uses Dynamics 365 Customer Insights to unify operational and behavioral data from fleet telemetry devices, warehouse automation sensors, driver scheduling platforms, shipment tracking systems, customer delivery feedback, mobile driver safety apps, and third-party partner logistics networks. They want to compute a “Logistics Performance Efficiency Index” that evaluates how efficiently each driver or fleet unit performs, based on on-time delivery rate, route optimization adherence, vehicle telemetry health stability, number of safety app interactions, shipment exception frequency, and warehouse automation alignment over the last 150 days. This performance index must be recalculated automatically and appear as a profile attribute for segmentation, operational journeys, predictive maintenance modeling, and driver performance coaching. Which Customer Insights capability should they use to compute this index?
A) Create a segment grouping drivers by efficiency
B) Build a measure to calculate the performance efficiency index
C) Use deterministic matching to identify efficient drivers
D) Import the index manually every six months
Answer: B
Explanation:
This scenario illustrates an advanced operational analytics requirement involving multiple data sources across a supply chain ecosystem. The logistics provider collects detailed interaction and telemetry data from numerous systems: fleet management, automated warehouses, shipment tracking, safety programs, and telemetry devices. They want to compute a Logistics Performance Efficiency Index that evaluates performance holistically. Because the index requires aggregating multiple behavioral, operational, and sensor-based data points within a rolling time window of 150 days, measures are the correct Customer Insights feature.
Vehicle telemetry health stability is another dimension. Telemetry from connected vehicles includes engine diagnostics, brake performance, fuel efficiency, tire pressure, idle time, and mechanical anomalies. Stable telemetry readings often correlate with smooth operations. Measures can aggregate telemetry health metrics and normalize them into the index.
Safety app interactions reflect driver engagement with safety protocols. These apps track distraction-free driving, alert responses, environmental hazard reporting, and safety module completions. Higher interaction levels typically correlate with safer and more efficient behavior. Measures allow counting and weighting these interactions.
Option A is incorrect because grouping drivers into segments cannot compute the index. Segmentation only categorizes profiles based on attributes that exist after the index has been calculated.
Option C is incorrect because deterministic matching helps unify profiles, not compute performance metrics. It cannot interpret telemetry, delivery patterns, or safety behaviors.
Option D is incorrect because manually importing the index every six months contradicts the need for continuous recalculation. Operational data updates constantly as drivers complete routes, shipments move, and telemetry streams into the system.
Measures are uniquely capable of:
Time-window bound calculations (ex: last 150 days)
Combining numeric values from multiple sources
Supporting advanced weighting and normalization
Automatically recalculating when new data flows in
Refreshing profile attributes in real time
Once the index is calculated as a measure, the logistics provider can use it in segmentation to identify high-performing drivers for recognition programs, low-performing drivers for coaching, or vehicles that show early signs of predictive maintenance needs. Operational journeys can automate alerts for performance drops or rising exception frequency trends.
Because measures are the only Customer Insights feature capable of dynamically computing a multi-factor operational performance index, option B is correct.
Question 91:
A multinational retail apparel corporation uses Dynamics 365 Customer Insights to unify customer data from point-of-sale transactions, RFID-based inventory interactions, mobile shopping app usage, loyalty program histories, e-commerce browsing sessions, virtual fitting room heatmaps, and post-purchase styling consultations. They want to compute a “Fashion Engagement Depth Score” that analyzes recency of mobile app engagement, number of virtual fitting interactions, frequency of personalized styling consultations, volume of omni-channel purchases, and behavioral consistency across seasonal fashion cycles over the last 180 days. The score must refresh automatically and be available as a numeric profile attribute for segmentation and personalized fashion journeys. Which Customer Insights capability should they use to compute this score?
A) Create a segment to classify customers by fashion engagement
B) Build a measure that aggregates engagement depth behaviors
C) Use deterministic matching to identify highly engaged shoppers
D) Import the engagement score manually twice a year
Answer: B
Explanation:
In this scenario, the retail apparel corporation unifies a wide array of customer interaction data inside Dynamics 365 Customer Insights, including offline shopping behaviors, online browsing activity, RFID interactions with products, mobile app usage patterns, and styling consultation history. They want to compute a Fashion Engagement Depth Score—a numeric value that represents the intensity and depth of a customer’s relationship with the brand. Because the score is based on several behavioral factors and must refresh automatically with new data, measures are the correct Customer Insights capability to use.
Measures are designed to calculate numeric values using aggregated interactions, recency filters, weighted factors, and multi-source data. The score in this case requires interpreting omni-channel activity, including virtual fitting room usage. Virtual fitting rooms produce heatmaps and interaction logs whenever customers try on outfits using augmented reality tools. Measures allow these events to be counted, weighted, and filtered by time windows, such as the last 180 days.
App engagement recency is another critical factor. Fashion brands rely heavily on mobile apps to drive engagement, recommend outfits, and promote seasonal collections. Recency filters help determine how recently a customer opened the app, viewed personalized look books, or engaged with content like style quizzes. Measures can capture these signals and build them into the scoring model.
Personalized styling consultations—both in-store and virtual—represent high-value engagement. Customers who book multiple styling sessions often have strong brand loyalty. Measures allow these interactions to be included in weighted calculations.
Omni-channel purchase volume integrates transactions across physical stores, e-commerce sites, mobile shopping, and social commerce channels. Measures can compute total purchase volume, diversity of product categories purchased, and purchase recency.
Seasonality trends are another essential component. Fashion engagement often follows seasonal cycles such as summer, fall, holiday, and spring collections. Behavioral consistency across seasons indicates deep engagement. Measures allow organizations to define rolling time windows (like 180 days) and identify whether behaviors remain consistent over these cycles.
Option A is incorrect because segments cannot compute numeric scores. They only group customers based on attributes that exist after measures have calculated them.
Option C is incorrect because deterministic matching only merges duplicate profiles. It does not evaluate engagement, interpret omni-channel behaviors, or calculate numeric values.
Option D is incorrect because manually importing a score every six months contradicts the requirement for dynamic recalculation. Mobile app behaviors and online browsing patterns change frequently, and manual imports would produce outdated insights.
Measures uniquely allow the retailer to combine numerous behavioral factors into a single dynamic score that recalculates whenever new interaction data enters the system. The resulting profile attribute can be used for segmentation, personalized journeys, seasonal marketing campaigns, or targeted styling recommendations.
Thus, the correct answer is option B.
Question 92:
A worldwide education technology company uses Dynamics 365 Customer Insights to unify learner data from online course platforms, virtual classroom attendance logs, mobile study app interactions, certification exam attempts, AI-powered skill assessments, instructor feedback, and peer collaboration tools. Their external AI engine generates a monthly “Learner Skill Mastery Probability Score” predicting how likely each learner is to pass advanced certification exams within the next 90 days. They want this score imported automatically every month, mapped to unified learner profiles, and used for segmentation, mentorship program journeys, and targeted upskilling pathways. Which Customer Insights integration method should they use?
A) Manually enter the mastery probability score monthly
B) Import the predictive score as an enrichment table
C) Use segmentation to approximate mastery probabilities
D) Use deterministic matching to calculate probability values
Answer: B
Explanation:
This scenario describes a global education technology company using Customer Insights as a centralized hub for learner data. They integrate rich engagement information such as virtual classroom attendance, online course progress, AI-driven assessments, instructor comments, and mobile app study patterns. However, the Learner Skill Mastery Probability Score comes from an external predictive engine that runs monthly and must be imported back into Customer Insights. The correct method for accomplishing this is to use enrichment tables.
Enrichment tables are specifically designed to ingest external datasets and map them to unified profiles on a recurring schedule. Since the AI system generates predictive scores automatically every month, the education company needs a consistent ingestion process. Enrichment allows them to upload the dataset, match learners using values like learner ID or email, and add the score as a new attribute to each unified learner profile.
Option A is not feasible because manually entering scores every month is inefficient, error-prone, and impractical for organizations with thousands or millions of learners. The requirement explicitly states that the import must be automated.
Option C is incorrect because segmentation cannot approximate or calculate probability values. Segments classify profiles according to attributes but do not perform analytical scoring or predictive modeling.
Option D is incorrect because deterministic matching simply identifies duplicate profiles. It cannot analyze learning behaviors or compute probability scores.
Once the enrichment table is ingested, Customer Insights can use the mastery probability score across the full ecosystem. Segments can identify learners with high probability who need accelerated pathways, learners with low probability who need intervention or mentoring, and learners with medium probability who need personalized study content.
The score can also power learning journeys—such as automated nudges, recommended courses, reminders for certification attempts, or custom study schedules.
Thus, enrichment is the correct integration method, making option B the accurate answer.
Question 93:
A global sports performance and athlete analytics organization uses Dynamics 365 Customer Insights to unify data from athlete training devices, performance tracking wearables, coaching session logs, nutritional program adherence, injury rehabilitation apps, competition schedules, and biometric monitoring sensors. They want to compute a “Performance Consistency Strength Score” based on training frequency, biometric stability, adherence to nutrition plans, recency of coaching interactions, injury recovery milestones, and competition readiness indicators over the last 150 days. The score must refresh automatically, be added to athlete unified profiles, and fuel segmentation and performance improvement journeys. Which Customer Insights capability should they use to compute this score?
A) Create a segment classifying athletes by consistency
B) Build a measure to calculate the performance consistency strength score
C) Use deterministic matching to identify performance patterns
D) Import the performance score manually at the end of each season
Answer: B
Explanation:
This scenario involves an athletic training and performance analytics organization that unifies high-frequency athlete data inside Customer Insights. Training device metrics, biometric readings, injury rehabilitation logs, nutrition adherence patterns, coaching sessions, and competition readiness indicators all contribute to an athlete’s performance consistency. Computing a Performance Consistency Strength Score requires aggregating multiple time-based and behavioral factors, which makes measures the correct solution.
Training frequency is one of the strongest indicators of performance consistency. Athletes who train regularly demonstrate higher discipline and greater readiness for competition. Measures allow training session logs to be aggregated and evaluated across a rolling 150-day period.
Biometric stability includes heart rate variability, recovery times, sleep data, and oxygen saturation. Wearable devices generate these biometric readings continuously. Measures can average biometric stability scores and incorporate them into the consistency model.
Nutrition adherence is also essential. Many athletes follow structured meal plans that contribute to performance. Deviations from nutritional programs can affect readiness and overall consistency. Measures can track adherence events and compute weighted values.
Coaching session recency provides insight into how often athletes receive technical guidance, strategy adjustments, or rehabilitation coaching. Measures can evaluate recency and frequency simultaneously.
Injury recovery milestones matter because athletes often follow rehabilitation pathways. Reaching milestones on time demonstrates consistency. Measures can monitor milestone logs and incorporate them into the score.
Competition readiness indicators—such as event registrations, performance simulations, and readiness assessments—are also strong predictors. Measures can count readiness-related interactions and integrate them.
Option A is incorrect because segmentation cannot compute numerical scores. Segments only classify athletes after the measure calculates the value.
Option C is incorrect because deterministic matching only identifies duplicate athlete profiles, and has no analytical capability.
Option D is incorrect because manual imports at the end of each season would not reflect daily or weekly changes in training, biometrics, or nutrition. The score must refresh automatically.
Measures allow the organization to compute, refresh, and maintain a dynamic performance consistency score that powers segmentation and personalized training journeys, making option B the correct answer.
Question 94:
A multinational home automation and smart-living technology corporation uses Dynamics 365 Customer Insights to unify customer data from smart thermostat usage, security camera event logs, smart lighting schedules, home energy efficiency insights, predictive maintenance alerts, voice-assistant command histories, mobile app interactions, and customer support cases. They want to create a “Smart Home Engagement Responsiveness Score” based on recency of mobile app control actions, frequency of voice-assistant commands, number of automation routines executed, adherence to energy-optimization recommendations, and stability of connected device telemetry over the last 100 days. This score must be recalculated automatically as new IoT events stream into the system and must be stored as a numeric profile attribute for segmentation and proactive engagement journeys. Which Customer Insights capability should the company use to compute this score?
A) Create a segment to classify smart-home users by responsiveness
B) Build a measure that calculates the engagement responsiveness score
C) Use deterministic matching to identify responsive households
D) Import the score manually every quarter
Answer: B
Explanation:
This scenario describes a smart-living ecosystem in which each household interacts with an extensive array of connected devices. These interactions include smart thermostat adjustments, automated lighting events, motion detection logs from security cameras, voice-assistant commands used to control devices, and mobile app interactions. Additional factors such as energy-efficiency guidance adoption, predictive maintenance alerts, and system health telemetry also contribute to how responsive a household is to smart-home technology. The goal is to compute a Smart Home Engagement Responsiveness Score that dynamically evaluates how engaged and responsive customers are over the last 100 days.
Measures are built precisely for this type of calculation. A measure in Customer Insights can aggregate events from various connected device interactions, apply weighted logic, calculate recency-based factors, and refresh automatically when new data streams into the system.
The factors that contribute to the score demonstrate why a measure is needed. Recency of mobile app control actions is essential because smart-home engagement is heavily tied to mobile interaction. When users actively adjust devices through the app—like modifying temperatures, checking camera feeds, or controlling lighting—the system captures these events and stores them as interactions. Measures can compute how recent and frequent these actions are.
Voice-assistant commands represent another dimension of engagement. Modern households often rely on smart speakers or voice-enabled devices to perform actions such as turning on lights, checking energy usage, locking doors, or initiating routines. Measures can count the number of voice commands issued within the last 100 days and incorporate them into the responsiveness score.
Automation routines represent proactive user behavior. These routines include nightly home-locking sequences, temperature-scheduling scripts, energy-saving routines, and motion-triggered lighting scenarios. Measures can evaluate automation routine execution frequency because these reflect high levels of system adoption.
Energy-optimization adherence is a particularly valuable factor. Smart-home platforms often provide recommendations to improve energy efficiency, such as lowering thermostat settings during certain hours or optimizing lighting brightness. Measuring how often users follow these recommendations requires aggregation and interpretation of energy-related interactions—something a measure can accomplish.
Connected device telemetry stability is another complex signal. Telemetry includes device health, network connectivity, battery status for wireless devices, motion sensor reliability, and video quality in security cameras. Stable telemetry suggests a household is actively maintaining its devices and interacting with them regularly.
Option A is incorrect because segmentation cannot compute numeric scores. While segments can classify households after the responsiveness score has been calculated, they cannot produce the score themselves.
Option C is incorrect because deterministic matching only merges duplicates. It has no capability to compute engagement scores, interpret telemetry logs, or analyze recency of commands.
Option D is also incorrect. Manually importing the responsiveness score every quarter would not support real-time smart-home operations. IoT data streams are continuous, and engagement scores must reflect recent behavior. Waiting for quarterly updates would misrepresent user responsiveness and undermine personalized journeys.
Measures address all these challenges by enabling dynamic recalculation and generating a numeric profile attribute that reflects multi-source aggregated behaviors. Once computed, this score becomes a central signal for segmentation—such as identifying users who are highly engaged, moderately engaged, or disengaging. It can also drive proactive engagement journeys such as sending maintenance reminders, offering smart-home upgrade suggestions, or enabling energy-efficiency campaigns.
Thus, building a measure is the correct answer, and option B is the right choice.
Question 95:
An international travel hospitality chain uses Dynamics 365 Customer Insights to unify guest data from hotel check-ins, digital room-key usage, mobile concierge app interactions, spa and dining reservations, loyalty reward redemptions, housekeeping service requests, location-based beacon engagement, and post-stay feedback surveys. Their data science team produces a monthly “Guest Loyalty Expansion Potential Score” that estimates how likely each guest is to upgrade to premium membership tiers or adopt additional hospitality services. They want this score ingested into Customer Insights automatically every month and mapped to unified guest profiles for segmentation, personalized hospitality journeys, and predictive service modeling. Which Customer Insights integration method should they use?
A) Enter each guest’s potential score manually
B) Import the predictive dataset as an enrichment table and map it to unified profiles
C) Use segmentation to approximate loyalty expansion potential
D) Use deterministic matching to generate loyalty scores
Answer: B
Explanation:
This scenario describes a hospitality chain that operates globally, with guests engaging across many digital and physical touchpoints. These interactions include digital room keys, app-based concierge requests, loyalty redemptions, spa bookings, dining reservations, and room service requests. The organization uses an external predictive model to generate a Guest Loyalty Expansion Potential Score each month. They want this score imported automatically and mapped into Customer Insights. Enrichment tables are the precise and correct method for achieving this.
To understand why enrichment tables are required, consider how the predictive model works. The data science team uses several factors to generate a monthly loyalty expansion potential score. These factors may include recency and frequency of loyalty reward utilization, cross-property visit patterns, engagement with premium amenities like spas or lounges, consistency of mobile concierge usage, booking patterns for suites versus standard rooms, and post-stay satisfaction scores.
Because these variables are diverse and require specialized predictive modeling, the score is produced external to Customer Insights. Therefore, Customer Insights must ingest this score as a dataset. Enrichment tables allow organizations to import external datasets and map them directly to unified profiles using unique identifiers such as guest ID, loyalty ID, or email address.
Option A is incorrect because manually entering the scores is not practical for a large hospitality organization. With potentially millions of guests, manual entry is slow, error-prone, and impractical for updating scores monthly.
Option C is incorrect because segmentation cannot approximate or compute predictive values. Segments classify profiles based on existing attributes. They cannot analyze hospitality behavior patterns or generate numeric probability scores.
Option D is incorrect because deterministic matching only merges duplicate profiles across source systems. It cannot perform predictive modeling or generate loyalty expansion values.
When enrichment tables ingest the dataset each month, the scores become first-class profile attributes within Customer Insights. This enables powerful downstream actions:
Segments can classify guests into categories such as high-potential, medium-potential, or low-potential for premium membership upgrades. Hospitality journeys can use the scores to deliver personalized upgrade offers, suite-level incentives, or premium dining experiences. Predictive models inside Customer Insights may also use the imported score as an input to forecast long-term loyalty behaviors.
Enrichment tables also support scheduled refresh intervals. This is essential because the predictive model updates monthly. Customer Insights must automatically retrieve the new dataset and update profiles without manual involvement. Enrichment pipelines provide seamless, reliable, and structured data ingestion.
Because enrichment tables fulfill all requirements—automated ingestion, mapping, profile attribute creation, and downstream activation—they are the only correct solution. Therefore, option B is correct.
Question 96:
A global telemedicine provider uses Dynamics 365 Customer Insights to unify patient data from virtual appointment logs, mobile health app interactions, AI-based symptom checkers, remote monitoring device telemetry, prescription refill histories, wellness program participation, and patient satisfaction surveys. They want to calculate a “Virtual Care Engagement Stability Score” based on recency of virtual consultations, frequency of symptom-checker usage, adherence to remote monitoring schedules, mobile-app activity depth, and consistency of wellness program engagement over the last 120 days. The score must update automatically and be available as a numeric attribute for segmentation and automated care-journey triggers. Which Customer Insights capability should they use to compute this score?
A) Create a segment to classify virtual-care patients
B) Build a measure that computes engagement stability
C) Use deterministic matching to detect stable patients
D) Import the score manually every six months
Answer: B
Explanation:
This scenario requires calculating a Virtual Care Engagement Stability Score using several dynamic patient behaviors. Customer Insights measures are specifically designed for this purpose because they can aggregate multiple interaction types, apply recency filters, evaluate trends, and compute numerical values that refresh in real time. A measure is ideal for scenarios that require interpreting and scoring large volumes of behavioral data from multiple sources.
The telemedicine provider collects diverse interactions. Virtual appointment logs show how frequently patients interact with clinicians through remote consultations. Recency is essential because a patient who had a consultation two days ago displays stronger engagement than one who has not scheduled a session in months. Measures allow these interactions to be weighted by freshness and frequency.
Symptom-checker usage provides another behavioral dimension. Patients may enter symptoms into AI-powered tools that generate preliminary assessments. Frequent use indicates a proactive attitude toward self-care. Measures can aggregate the number of symptom-checker sessions over the last 120 days and incorporate their weighted contribution to the score.
Remote monitoring device telemetry is another critical factor. Devices like glucometers, heart-rate trackers, pulse oximeters, or blood pressure monitors send data at regular intervals. Stability and frequency of these telemetry readings reflect consistent adherence to remote-care protocols. Measures can evaluate telemetry volume and stability within the 120-day timeframe.
Mobile health app activity depth includes features such as medication reminders, appointment scheduling, educational content, and messaging. Measures can interpret how deeply a patient uses these features and how recently they engaged with them.
Wellness program engagement consistency—participation in exercise programs, mental-health modules, or lifestyle coaching—is another indicator. Patients who consistently follow their wellness programs are typically more engaged with their long-term care plan. Measures allow integration of these behaviors alongside other data streams.
Option A is incorrect because segmentation itself cannot compute or aggregate engagement data. Segments only classify profiles after the numeric score already exists.
Option C is incorrect because deterministic matching simply cleans duplicate records. It does not compute numerical values or interpret patient behavior patterns.
Option D is incorrect because manual imports every six months would ignore the provider’s requirement for automated, ongoing scoring. Telemedicine activity changes weekly or daily, making manual updates ineffective and outdated.
Measures allow real-time recalculation as new data arrives from remote monitoring devices, symptom-checker tools, wellness apps, and appointment systems. Once the score becomes a profile attribute, it fuels segmentation and automated care journeys, such as recommending a follow-up appointment or sending reminders for wellness participation.
Thus, the correct answer is B.
Question 97:
A global e-commerce marketplace uses Dynamics 365 Customer Insights to unify customer behavior from product browsing logs, abandoned-cart patterns, purchase histories, loyalty rewards, customer-support chats, mobile app activity, and marketing email interactions. They want to import a monthly “Purchase Propensity Probability Score” generated by their external machine learning system. The score predicts each customer’s likelihood of completing a purchase in the next 30 days. They need the score automatically ingested every month and mapped to unified profiles for segmentation and personalized product-recommendation journeys. Which Customer Insights integration method should they use?
A) Enter purchase propensity scores manually
B) Import the ML output as an enrichment table
C) Use segmentation to estimate propensity
D) Use deterministic matching to calculate probability
Answer: B
Explanation:
The e-commerce marketplace already uses a machine-learning model outside Customer Insights to generate a Purchase Propensity Probability Score. Because this score is produced externally and must be regularly refreshed, the correct Customer Insights capability to ingest the dataset is enrichment tables. Enrichment allows structured import of external predictive data, mapping the results to existing unified profiles.
The organization processes extensive behavioral data. Browsing logs reveal the types of products users explore. Abandoned-cart patterns show whether the user frequently leaves items unpurchased. Purchase history indicates previous buying rhythms, product categories, and spending levels. Loyalty rewards provide insight into long-term engagement. Chat interactions show customer service needs and sentiment. Mobile app interactions reveal usage depth and recency. Marketing email activity reflects responsiveness to promotions and content.
The machine learning model consumes all these variables and generates a probability score estimating how likely each customer is to purchase within the next 30 days. Because the output is external, Customer Insights does not compute this score internally. Thus, the system must import the score data using an enrichment dataset, mapping it to customer profiles with a unique identifier such as user ID or email.
Option A is incorrect because manually entering thousands or millions of propensity scores each month is impractical, error-prone, and does not scale. It also violates the requirement for automatic ingestion.
Option C is incorrect because segmentation cannot compute complex probability values. Segments can use the score once imported but cannot generate it.
Option D is incorrect because deterministic matching merges duplicate customer profiles; it does not perform predictive modeling.
Enrichment tables support scheduled ingestion, which is essential because the predictive model updates monthly. Customer Insights can automatically import the new dataset, map it to profiles, and update any downstream segmentation or personalization.
Once imported, the score becomes extremely valuable. The marketplace can create high-propensity segments for targeted promotions or identify low-propensity users who need engagement nudges. Personalized journeys can recommend products, offer discounts, or re-engage users who abandoned carts.
Since enrichment tables are the only feature capable of importing external predictive scores in an automated fashion, the correct answer is B.
Question 98:
A multinational manufacturing company uses Dynamics 365 Customer Insights to unify distributor data from order histories, B2B portal logins, equipment registration databases, warranty activations, technical support logs, field service visit histories, and training certification completions. They want to compute a “Distributor Engagement Maturity Score” considering purchase recurrence, support-ticket patterns, training completion frequency, portal-engagement recency, and warranty activation trends over 200 days. The score must refresh automatically and appear as a profile attribute to power segmentation and dealer enablement journeys. Which Customer Insights capability should they use to compute this score?
A) Create a segment to filter mature distributors
B) Build a measure that calculates engagement maturity
C) Use deterministic matching to detect maturity patterns
D) Import the maturity score manually twice per year
Answer: B
Explanation:
This scenario requires calculating a Distributor Engagement Maturity Score based on multiple behavioral and operational signals. A measure is required because Customer Insights measures allow multi-condition, multi-source numeric scoring that updates automatically as new data enters the system. The manufacturing company collects extensive distributor activity patterns that vary significantly across accounts. A measure is the only way to aggregate these behaviors over a defined window, such as 200 days.
Purchase recurrence is a strong indicator of distributor maturity. Partners who place orders regularly demonstrate deeper engagement. Measures can evaluate transaction frequency and recency to assign weighted contributions to the maturity score.
Portal logins reveal digital engagement. Distributors who frequently log into the B2B portal to track orders, download documentation, or request quotes show higher engagement. Measures can compute recency and volume of logins.
Equipment registrations indicate how actively distributors enroll new machinery into the system. High registration counts reflect strong downstream customer relationships and product adoption.
Warranty activations help determine product lifecycle engagement. Distributors who manage warranty processes effectively tend to have strong operational alignment with the manufacturer.
Support-ticket patterns reflect technical capability. A distributor filing fewer repetitive or unnecessary tickets may have higher internal competency. Measures allow ticket analysis over time.
Training certification frequency is also essential. A distributor committed to ongoing training demonstrates deeper alignment with the manufacturer’s standards. Measures can incorporate the number and recency of course completions.
Option A is incorrect because segmentation cannot compute maturity scores; it only groups profiles using attributes that already exist.
Option C is incorrect because deterministic matching cleans duplicates and does not interpret behavioral signals.
Option D is incorrect because manually uploading scores twice per year contradicts the need for continuous automated scoring. Distributor engagement behaviors change regularly as service tickets, training completions, and order volumes fluctuate.
Measures provide the required numerical calculation capability, real-time updates, and integration with segmentation and journeys. Therefore, the correct answer is B.
Question 99:
A global subscription-based streaming media provider uses Dynamics 365 Customer Insights to unify subscriber data from content viewing histories, search queries, recommendation click-throughs, watchlist additions, device sign-ins, ad-skipping behavior, and in-app feedback ratings. They want to compute a “Content Discovery Curiosity Score” that evaluates how actively subscribers explore new content based on: recency of platform usage, breadth of genres explored, number of first-time title plays, frequency of watchlist additions, interaction with personalized recommendations, and stability of multi-device engagement over the last 160 days. This score must be recalculated automatically and stored as a numeric profile attribute to power segmentation and personalized discovery journeys. Which Customer Insights capability should they use to compute this score?
A) Create a segment to classify curious subscribers
B) Build a measure that calculates the curiosity score
C) Use deterministic matching to identify exploratory viewers
D) Import the curiosity score manually every quarter
Answer: B
Explanation:
The streaming provider wants to compute a dynamic, multi-factor behavioral score based on how subscribers discover and explore new content over a specific time window (last 160 days). This requires aggregating several interaction types—new title plays, cross-genre exploration, watchlist additions, recommendation engagement, and multi-device stability—and converting them into a single numeric score that updates as new data flows in. In Dynamics 365 Customer Insights, measures are the correct capability for this scenario.
Measures can:
Aggregate interaction events from multiple sources (viewing logs, search, recommendations, watchlists)
Apply recency filters (for example, last 160 days)
Weight different behaviors (for example, first-time plays vs. repeat views)
Recalculate automatically when new interactions are ingested
Output a numeric attribute directly on the unified profile
Option A is incorrect because segments group subscribers based on existing attributes; they do not compute a numeric curiosity score. Segments can later use the score (for example, “Curiosity Score > 80”), but they cannot create it.
Option C is incorrect because deterministic matching is used to merge duplicate subscriber profiles across systems (such as combining accounts from mobile, TV, and web). It does not analyze viewing behavior or calculate curiosity.
Option D is incorrect because manually importing a curiosity score every quarter conflicts with the requirement for continuous, automatic recalculation as subscribers’ exploration patterns change daily.
Because only measures provide automated, multi-source numerical scoring that refreshes as new behavioral data arrives, option B is the correct answer.
Question 100:
A multinational health and wellness retailer uses Dynamics 365 Customer Insights to unify data from in-store purchases, e-commerce orders, loyalty program activities, nutrition consultation bookings, fitness class check-ins, mobile wellness app usage, and post-purchase survey feedback. An external analytics system already calculates a monthly “Holistic Wellness Growth Probability Score” for each customer, estimating how likely they are to expand into higher-value wellness bundles and long-term subscription plans. They want to import this score automatically every month, map it to unified customer profiles, and use it for segmentation, targeted wellness subscription journeys, and predictive upsell modeling. Which Customer Insights integration method should they use?
A) Manually key in the wellness growth probability for each customer every month
B) Import the external analytics output as an enrichment table and map it to unified profiles
C) Use segmentation rules to approximate wellness growth probabilities
D) Use deterministic matching to generate the probability scores from transactional data
Answer: B
Explanation:
This scenario describes an external analytics system that already computes a Holistic Wellness Growth Probability Score each month. The retailer wants this predictive score brought into Customer Insights on a recurring basis, mapped to unified profiles, and then used like any other attribute for segmentation and journeys. The correct approach in Customer Insights is to use enrichment tables.
Enrichment tables are designed to:
Ingest external datasets (for example, CSV or data lake exports) on a scheduled basis
Map those records to unified profiles using identifiers (customer ID, loyalty ID, email, etc.)
Treat the imported fields (such as probability scores) as native profile attributes
Refresh values automatically as new files are ingested each month
Option A is incorrect because manually entering monthly scores does not scale across thousands or millions of customers and contradicts the requirement for automated, recurring updates.
Option C is incorrect because segmentation cannot calculate or approximate probability scores produced by a machine learning model. Segments can only filter or group customers based on attributes that already exist, such as “Probability Score ≥ 0.7,” once the score has been imported.
Option D is incorrect because deterministic matching only resolves duplicate customer records across sources; it does not perform predictive analytics or generate probability scores from raw transactions.
Because enrichment tables are the only Customer Insights capability specifically built to ingest and map recurring external predictive scores into unified profiles, option B is the correct answer.
