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Top Five Ways to Apply Advanced Analytics and AI

Analytics, and in many cases spreadsheets, has been the bastion of the back office for retailers and consumer products companies for years — understanding volumes, margins, geographic performance, and SKUs. But recently, something fundamental has changed. Executives are pushing for the next level of analytics to uncover the hidden opportunities in data to create powerful and differentiated product, customer, and brand experiences.

Why? It’s because the competitive game has changed. Customer service has now become as crucial to customer loyalty as the product itself. Customers expect seamless personalized experiences, in-store, online, across channels, and SKUs.

The challenge is that traditional analytics hasn’t kept up, with analysts overwhelmed by so many different customer journeys, a spiraling variety of products, and the need to connect signals from service, social, and other channels. It’s not only about applying artificial intelligence and machine learning to tease out opportunities in data, but breaking analytics out of the back office, and putting actionable insights in the hands of those at the frontline like field reps, service agents, logistics and category managers.

Here are five ways retailers and CPG companies can apply the latest analytics to compete smarter:

1. Uncover the hidden opportunities customer value
Whether it’s proximity or service in retail, or trust and price in CPG, understanding and acting on the drivers of loyalty is foundational. In a consumer survey of 14,700 adults, 40% said they make repeat purchases but are not loyal to a company, according to a Facebook IQ Survey. Last year, McKinsey found that companies who understand their customers and the best channels to reach them outperform their category by up to 16 percentage points.

Getting there means not only integrating marketing, sales and service data, but applying the latest AI and machine learning capabilities to analyze data across not just a handful of dimensions, but tens or hundreds of attributes — and that means guiding analysts on precisely where to drill down, asking the next questions, and using machine intelligence to help answer questions they didn’t think to ask.

2. Shift up product pricing and promotion planning
If your analytics for designing promotions hasn’t moved way beyond the SKU to target customers based on segment, then you’re leaving money on the table. With a wealth of data now available across all the channels of engagement, from the web to store, retailers can tap data science to help define better-performing promotions based on detailed customer behavioral and geographic trends, while also finding the optimal pricing to ensure they’re not leaving money on the table.

3. Tap the hidden lever of customer experience: inventory management
Consumers want online visibility into in-stock items at nearby stores, online ordering, with in-store pickup, and expect frictionless, low or no-cost ease of return — regardless of buying channel. Gathering purchasing data from web and store channels, AI/ML algorithms can identify purchasing patterns by geography, season or other dimensions drives more optimal regional and distribution center specific inventory decisions.

4. Setup the sales team to identify product and account whitespace
With extensive product catalogs, product variations, and operating on accelerated product lifecycles, it’s easy for CPG sales teams to miss opportunities to drive more value from current distributors or retailers. With modern analytics embedded in their everyday sales apps, CPG field reps can instantly see all their accounts, the products they’ve already purchased, and filter them by geography, product family, industry or more to focus their efforts, and get recommendations on where and what to sell next.

5. Tighten up the slack in retail execution
Optimizing the “last mile” from shelf to shopping cart is one of the biggest opportunities for retail and CPG alike. Done right, analytics can provide CPG field reps with a more intelligent experience from the recommendations on the right stores to visit based on drive time and opportunity, to actionable intelligence into the most impactful planograms to use in-store.

For example, Bain Insights references a case in which data supported a brand’s negotiations for more shelving real estate: “By arming its account managers with the information that the brand’s 30% share of shelf space was significantly lower than its 50% overall market share, one brand was better able to negotiate with store owners for more space.”

For CPGs and retailers alike, the age of the customer creates new challenges and incredible opportunities. With more SKUs, more customer journeys, changing consumer preferences and more distribution channels than ever, companies are turning to AI-driven insight that provides recommendations and prescriptions for the right strategies. And they’re looking to break analytics out of the back-office and put actionable data-driven decision making in the hands of everyone. What’s the next step you’ll take on your analytics journey to transform your customer experiences?

Keri Brooke is VP of product marketing for Einstein Analytics at Salesforce.
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