Succeed in uncertain times with AI/ML-based analytics

Today’s consumers are changing their budget priorities, product selections, channel choices, and buying journeys faster than ever. The influx of information sources, product innovations, ordering and fulfillment options, convenience and value alternatives, has made it difficult to obtain, let alone maintain, a wallet share among market customers. The share of customers of any kind is indeed the most crucial factor for the long-term success of a business. Yet customer strategy is often not a top priority, and without a current customer strategy, all other initiatives are likely to fail.

However, those who successfully pivot in today’s market will consider at least a few customer-centric initiatives, many of which can be supported by artificial intelligence/machine learning (AI/ML) analytics.

  1. Update consumer/buyer 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 buying behaviors and habits, attitudes and motivations, propensity and lifetime value, etc. Unlocked growth sits at the intersection of these dimensions, capitalizing on a holistic understanding of the customer.
  2. Identify the most valuable customers, including those who may not currently be customers: If approached correctly, segmentation analysis should reveal the most valuable customers in the market. While it’s helpful to leverage Customer Lifetime Value (CLV) scores, holistic segmentation analysis reveals more about the most valuable customers, their potential value, and ways to personalize engagement with them. .
  3. Understand customer needs, priorities and trade-offs: This requires continuous omnichannel tracking of shopper behaviors, including changing travel patterns, basket composition, and product switching across channels. Product purchases will reveal the underlying product attributes that matter most to shoppers today, such as price points, package sizes, certifications, benefits, and ingredient lists.
  4. Predict customer loyalty and provide personalized offers: Businesses with a loyalty program can predict likely stale shoppers and personalize 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 businesses around the world.

All four initiatives can be tracked, modeled, projected and predicted with AI/ML tools. For example, companies can maintain continuous algorithms to dynamically segment customers over time. Businesses can use open source code to model CLV scores, which can then be added to CRM/loyalty databases for customer targeting. Product attributes can be modeled and predicted to support product innovation. Inactive 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 by leveraging AI/ML Analytics

The wide availability of data clouds and advances in technology have made it easier to deliver AI/ML-based analytics at scale and quickly. Once businesses have prioritized their customers and goals, they can use AI/ML to model, predict, optimize, and prescribe many decisions for their business, including demand forecasting, pricing, promotion, marketing, and marketing. assortment, new product introduction, supply chain optimization and media.

Industry leaders today are using AI/ML analytics to influence customer demand in multiple ways.

  1. Identify product attributes, pack price changes, and price tiers: As buyers are increasingly constrained by inflationary and recessionary pressures, businesses will adjust their offerings to increase sales, exercises that AI/ML tools can easily support. Businesses are using AI/ML to forecast sales, share, and volume sources with high accuracy. Model coefficients are updated in near real time as new data is ingested. Updated information is being rolled out widely across all organizations.
  2. Optimization of price-value equations across channels and customers: Market leaders respond to inflation by reaffirming or shifting their value propositions to consumers, whether for premium products, many of which remain price inelastic, or for their value products, many of which reach price inflection points that alter elasticity slopes. Companies introduce product innovations with emerging and popular attributes that drive demand. Retailers market private labels to differentiate themselves and reduce price comparisons. Firms track price sensitivity by groups of buyers, distinguishing between primary and occasional buyers. This too is automated and supported by AI/ML tools.
  3. Promote products that companies can provide: Using AI/ML analytics, companies estimate promotional demand forecasts and sales uplift to inform their supply chain orders. They simulate scenarios with different goals and constraints, such as days of supply and fill rates. Analytics tools allow companies to quantify risks and opportunities with unprecedented accuracy.
  4. Increase omni-shelf productivity with the right space and assortment decisions: Omnichannel leaders use AI/ML analytics to generate store clusters, dendrograms, consumer decision trees, demand shift, incrementality, and substitution scores. Additionally, companies are optimizing space and assortment shifts in-store versus online, and facilitating those shifts online with better digital shelving, search, and digital media placement. They frequently perform and simulate A/B tests at scale and quickly to validate decisions.

It is precisely because of the ubiquity of information and the accessibility of advanced analytics that these features are available to everyone. AI/ML tools are not just for tactical decisions. They open up new possibilities for innovation and reinvention.

Innovate to prepare for a changing market landscape with AI/ML

Large enterprises are using AI/ML solutions to break out of current category and market boundaries. They analyze emerging category trends to identify new white space opportunities. They test new concepts with customers and conduct extensive quantitative experiments to select the right product offerings. They simulate portfolio, competition, trade and advertising changes to quantify the impacts on their business. Now more than ever, companies are using AI/ML tools as lightning-fast lightsabers for action today, and as crystal balls for longer-term insights with confidences that easily beat the odds. two-sided medal.

About Chris McCarter

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