Succeeding in Uncertain Times with AI/ML-Driven Analytics

With news of inflation and recession growing more frequent each day, retailers and their suppliers will be challenged to maintain relevance and loyalty with shoppers. How can companies pivot? A combination of business strategies and AI/ML tools.

Adobe Stock 194849644 (1)

Today’s consumers are changing budget priorities, product selections, channel choices and paths to purchases faster than ever before. The influx of information sources, product innovation, order and fulfillment options, convenience and value alternatives, has made it difficult to gain, let alone maintain, a share of wallet among customers in the market. Customer share of any kind is indeed the most crucial factor for a company’s long-term success. Yet, customer strategy is often not a top priority, and without a current customer strategy, all other initiatives risk failure.

However, those who pivot successfully in the current market will consider at least a couple customer-centric initiatives, many of which can be supported with artificial intelligence/machine learning (AI/ML) analytics.

  1. Updating consumer/shopper segmentations to reflect today’s realities: While there is no perfect segmentation, there are many combined approaches and practices that make segmentations actionable, including identifying behavioral and buying patterns, attitudes and motivations, propensity and lifetime value scores, etc. The growth unlocked is at the intersection of these dimensions, capitalizing on a holistic understanding of the customer.
  2. Identifying the most valuable customers, including those who might not be customers now: If approached correctly, segmentation analysis should reveal the most valuable customers in the market. While it’s useful to leverage customer lifetime value (CLV) scores, holistic segmentation analysis reveals more about the most valuable customers, their potential worth and ways to personalize engagement with them.
  3. Understanding customers’ needs, priorities, and trade-offs: This requires ongoing omni-channel tracking of shopper behaviors, including changing trip patterns, basket compositions and product switching across channels. Product purchases will reveal the underlying product attributes that matter most to buyers today, such as price points, pack sizes, certifications, benefit claims and ingredient lists.
  4. Predicting customer retention and delivering personalized offers: Businesses with loyalty program can predict likely lapsed buyers and customize offers to maintain customer recency and frequency. Loyal buyers can be targeted for upsell and cross-sell opportunities. AI/ML tools are making these options a reality for firms across the globe.

All four initiatives can be tracked, modeled, projected and predicted with AI/ML tools. For example, businesses can maintain ongoing algorithms to segment customers dynamically over time. Businesses can use open-source code to model CLV scores, which can then be appended to CRM/loyalty databases for customer targeting. Product attributes can be modeled and forecasted to support product innovation. Lapsed buyers can be predicted and engaged and, with the right data, science, technology, and expertise, businesses can automate opportunity detection and prioritization.

Accelerate Consumer Demand Leveraging AI/ML Analytics

The wide availability of data clouds and technology advancements have made it easier to deliver AI/ML-driven analytics at scale and speed. Once businesses prioritize their customers and objectives, they can use AI/ML to model, predict, optimize and prescribe many decisions for their business, including in demand forecasting, pricing, promotion, assortment, new product introductions, supply chain optimization and media.

Today’s industry leaders use AI/ML analytics to influence customer demand in myriad ways.

  1. Identifying product attributes, pack-price changes and price points: As shoppers become increasingly constrained by inflationary and recessionary pressures, companies will adjust their offers to grow sales—exercises that AI/ML tools can readily support. Firms are using AI/ML to forecast sales, share and sources of volume with high accuracy. Model coefficients are being updated in near-real time, as new data are ingested. Updated insights are being deployed at scale across organizations.
  2. Optimizing price-value equations across channels and customers: Market leaders are responding to inflation by reaffirming or shifting their value propositions to consumers, whether it’s for premium products, many of which remain price inelastic, or for their value products, many of which are hitting price inflection points that are changing elasticity slopes. Companies are introducing product innovation with emerging and popular attributes that spur demand. Retailers are commercializing private brands to differentiate themselves and lessen pricing comparisons. Firms are tracking price sensitivity by buyer groups, distinguishing between their core and occasional shoppers. This, too, is automated and supported by AI/ML tools.
  3. Promoting products that companies can supply: Using AI/ML analytics, companies are estimating promotional demand forecasts and sales lift to inform their supply chain orders. They are simulating scenarios with different objectives and constraints, such as days of supply and fulfillment rates. Analytic tools are enabling companies to quantify risk and opportunities with unprecedented accuracy.
  4. Increasing omni-shelf productivity with the right space and assortment decisions: Omni-channel leaders are using AI/ML analytics to generate store clusters, dendrograms, consumer decision trees, demand transference, incrementality and substitution scores. What’s more, companies are optimizing space and assortment changes in-store vs. online, and aiding those online changes with better digital shelving, searches and digital media placements. They are frequently conducting and simulating A/B testing at scale and speed to validate decisions.

It’s precisely because of the ubiquity of information and accessibility of advanced analytics that makes these capabilities possible for everyone. AI/ML tools are not just for tactical decisions. They open new possibilities for innovation and reinvention.

Innovate for Readiness Against a Changing Market Landscape With AI/ML

Leading firms use AI/ML solutions to think outside today’s category and market boundaries. They analyze emerging category trends to identify new white-space opportunities. They test new concepts with customers and conduct in-depth quantitative experiments to select the right product offers. They simulate portfolio, competitive, trade and advertising changes to quantify the impacts to their businesses. Now more than ever, companies are using AI/ML tools as lightning-fast lightsabers for action today, and as crystal balls for longer-term outlooks with confidences that handily beat the two-sided coin.