Joint Business Planning For Retailers: Then and Now
Historically, joint business planning requires significant investments in research and collaboration. Smart data strategies and AI are changing that equation forever.
Retailers and product manufacturers are natural allies. Both want to know what products customers are buying and why. Both want to leverage customer buying behavior to increase sales and profitability. Both want to fine-tune product messaging for effective marketing and advertising campaigns. Both possess vast amounts of relevant data. At first glance, aligning business goals would seem like second nature, but as with any other large-scale alliance, managing the details is complicated.
For retailers and product manufacturers, alignment of business goals seems like second nature. But managing the details is complicated.
The process of Joint Business Planning (JBP) is typically undertaken with the help of research firms and consultancies, often at considerable cost. Once the retailer and the manufacturer have identified mutual opportunities, strategies, and goals—supported by surveys and case studies—they proceed with a business plan. The plan identifies promising product categories and tasks the retailer’s merchandising planners and marketing and advertising directors with promoting them.
For the majority of retailers, even those with access to mountains of data, detailed JBP projects are unrealistic.
Needless to say, this is a costly, time-consuming process. Retail giants like Wal-Mart and equally gigantic manufacturers like Procter & Gamble can afford comprehensive JBP projects, such as their successful plan to increase sales of Febreze. But for most retailers, even those with access to mountains of data, such detailed planning is unrealistic—or at least it was.
Data: The Secret Sauce
Retailers of every size are awash in data. Manufacturer-supplied data for thousands or millions of products inhabit their dedicated product information management (PIM) systems. Images, audio and video files, customer reviews, and promotional descriptions for each product are housed in vast digital asset management (DAM) systems. Product pricing, inventory, and sales history data may also have their respective data repositories. That’s all well and good, but the real question is how to use all that data effectively.
Even with abundant data, marketing and advertising directors have needed help to precisely plan the product offers with the greatest potential for success. Their instincts are often sound—as are those of a campaign’s merchandising planners. However, the results can be ineffective, considering so many variables. Joint business planning, if it occurred at all, required a manual review of legacy data.
Even with an abundance of product data, offer planning can become imprecise or ineffective.
The first step in resolving this puzzle is for the retailer to unify all that data as a “single source of truth.” Using an integrated approach, such as Comosoft LAGO, retailers can automate a wide variety of marketing and advertising production tasks and orchestrate complex, multi-region campaigns. However, the potential for retail marketing and advertising planners does not stop there.
Before AI can be used in offer planning, the data must be integrated and accessible.
Data Readiness and AI
The recent surge (or hype) surrounding artificial intelligence (AI) has piqued retailers’ interest in using product data more effectively. However, before considering that potential, the data must be readily
accessible to advertising and marketing leaders and their planning and production teams. In addition to being secure, the data must be of high quality—meaning it must be consistent and logically interconnected, which is the case with integrated planning and production systems like LAGO.
From such an integrated data foundation, AI can provide intelligent, actionable insights from structured and unstructured data—guiding the planning process of optimal product offerings. It does so by detecting patterns in the data, including:
Properly used, AI can guide the process of planning optimal product offerings.
- Shopper buying behavior, including purchase frequency changes and past responses to promotional campaigns.
- Product categories that are most likely increase a customer’s total purchases.
- Related products that customers typically buy or are likely to buy, based on history and display proximity.
- Optimal pricing estimates that are most likely to grow overall volume.
This and other “big data” potentials are not just theoretical. In a recent case study, a regional grocery chain realized a significant increase in sales resulting from promotional offers produced in Comosoft LAGO and guided by AI modeling.
Far from magical or mysterious, AI is capable of finding practical, actionable insights hidden in the mass of product and purchase behavior data that all retailers possess. It can calculate customer behavior changes in response to past promotions and predicts likely outcomes for future ones. It can model interactions between
AI is not magical or mysterious. It merely finds actionable insights hidden in data that all retailers possess.
products at the customer level. It can analyze price point ranges by item to identify which weekly promotions will attract the greatest number of shoppers. This does not mean marketing and advertising directors or merchandising planners will become obsolete – far from it.
AI removes the drudgery of sorting through legacy data. (It also eliminates the need to hire expensive consultants to do the JBP legwork for you.) By using AI to supplement their intuitive, problem-solving abilities, retail marketing professionals can plan even better product promotions—ones that work best for their company’s long-term overall growth and profitability.
Find out more about Comosoft LAGO’s approach to AI-driven automation and its potential to transform your retail data and marketing strategy. Or book a demo to see for yourself.
