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Three ways AI is impacting your go-to-market strategy

In a previous post we reviewed the macro trends underlying the AI disruption. In practice, how does it impact go-to-market strategy? Let’s review the three areas primarily affected by these drivers.  In all cases the theme is the same: technology unlocks new capabilities. It enables leaders to operate faster and more efficiently, to identify new growth opportunities.

Customer Acquisition

Enhanced customer segmentation. Gone are the days of slow, manual segmentation studies that would be performed once a year at best. Clustering techniques such as K-Means, PCA, DBSCAN, and others allow us to segment customers and prospects in near real-time and identify micro-patterns invisible to the human analyst. We can now identify profitable segments and trends as soon as they emerge and take advantage of it before our competitors do.   

Targeting through better propensity scoring. We can now develop better lead propensity scoring using Random Forest, XGBoost, and other ensemble learning classification algorithms. This allows us to quickly and more efficiently differentiate between good leads and bad leads, increasing sales efficiency and lowering acquisition costs.



As always, technology unlocks new capabilities;

it enables leaders to identify new growth opportunities, and to operate
faster and more efficiently.

Scalable price targeting. Techniques such as LASSO, combined with any number of scoring algorithms, allows us to develop customer-level demand models at scale, which in turns enable customer-level pricing. Think airlines or Amazon targeting individual customers with individualized pricing. If implementation considerations are an issue this can support segmented pricing instead. Either way a more granular view of pricing, when supported by the proper operational systems and processes, can lead to significant revenue and bottom-line growth.

Sales performance management. Ever found yourself scrambling before a sales call to piece together a narrative for why sales are down? Anomaly detection and root-cause analysis techniques, combined with natural language processing, can now generate real-time insights as to why sales are up or down. This enables sales leaders to get a detailed understanding at all times of the drivers of sales performance. No more guessing who took share away and where, no more waiting for months until we find out the answer.   

Virtual sales assistants. You’ve probably dealt already with virtual sales assistants online. Natural language processing (NLP) has grown by leaps in recent years and virtual assistant are a great low-cost way of triaging leads online before hand-off to a live sales rep. It enables a more consistent sales experience online while also lowering unit acquisition costs.

Sales resources deployment and staffing. Machine-learning-enabled time series forecasting, coupled with driver-based modeling, enables accurate current-course-and-speed projections. Better demand and activity volumes projections allow us to deploy and staff sales functions more efficiently. 

Base Management and Care

Usage pattern detection and classification. Machine learning techniques such as PCA and others are surprisingly good at detecting changing patterns quickly. That’s why they were applied first in the areas of fraud detection and network security. They can also be applied to monitor usage patterns and identify customer experience issues before they arise, or spot growth opportunities as soon as they emerge.

Revenue performance management. Just as anomaly detection and root-cause analysis can help understand sales performance, it can also quickly identify changes in revenue within the customer base. Up-sell and cross-sell activity can be hard to track and analyze on an ongoing basis, ML-enhanced analysis can automate and speed up the process.

Next best action. One of the first applications of machine learning to customer management, next-best-action (NBA) systems, when calibrated and maintained properly, are great at consistently targeting customers with higher close rate offers that keep customers happy and coming back for more.

Revenue forecasting. Through the same logic AI can lead to better revenue forecasting by greatly expanding the type of data sources that can be fed into a forecast. Think of the impact of events, both local and macro, competitive drivers, etc.

Virtual care assistants. Enabled by the same NLP technology as the sales assistants, virtual care assistants lower care costs while providing a more consistent care experience. They have proven most effective when integrated with live reps in an augmented setup, as a means of triage.

Care resources staffing. Call centers traditionally rely on sophisticated staffing models. AI can expand the scope of such models by making it easier to increase the number of data sources and the speed at which such projections are produced. This leads to more granular, more accurate, and more frequent forecasts, which all combine to reduce operating costs. 

Customer Retention

Churn propensity scoring. Just as Random Forest and XGBoost can deliver better lead propensity scoring, it can also deliver better churn propensity scoring. Following a number of themes we’ve already explored, it can do so faster and at a more granular level, which in turn enables better targeted retention actions.

Early churn detection. Usage pattern detection and classification, discussed earlier, is a very effective approach to identify customers who may not technically be high-churn-propensity, but recently experienced a collections of events that are likely to trigger churn later on.

Churn driver identification. How many times have operational managers argued over what in fact drove a churn increase? Customer experience issues or price increase? Root cause analysis and causal inference are changing the game by providing an objective means to diagnose drivers.

Save offer selection. Better targeting and scoring means we can also better select the right save offer at any given time for customers being handled by save teams.

In summary, AI impacts virtually every aspect of go-to-market strategy. By greatly enhancing our ability to process information and identify quickly micro-patterns, AI is truly changing the game. We’ll explore these themes in more detail with practical examples in upcoming posts.

About Pierre Elisseeff
Pierre has worked in the communications, media and technology sector for over 20 years. He has held a number of executive roles in finance, marketing, and operations, and has significant expertise leading business analytics teams across a broad set of functions (financial analytics, sales analytics, marketing and pricing analytics, credit risk).