From managing at the mean to managing at the meaningful.
The competitive landscape of the retailers’ shelf is changing, with new capabilities redefining how manufacturers and retailers work together to gain their share of customer money. The ability to use operational point of sale (POS) data to redefine how companies go to market, execute against strategies, manage product availability and drive spending through measurement and incentives is central to this shift, found a new study by Booz & Company.
“Operational POS data provides next-day information into what stock keeping units (SKUs) sold, in what store, at what price, with information on remaining stock,” commented Fabrice Saporito, a Principal with Booz & Company. This information signals a change in the consumer packaged goods (CPG) industry: a recent AMR Research survey found 56% of manufacturers require two or more weeks to know shelf demand because of lagging POS data, and 19% require a month or longer.
Long response times mean more out of stocks (OOS), and limited customer insights hinder adjusting go-to-market vehicles such as price and promotion in time to improve sales. Retailers also provide POS data to manufacturers late. “Instead of managing to the meaningful – data on a given item, on a given day, at a given store, retailers and manufacturers are managing to the mean – using aggregate data that doesn’t reflect what happens on the shelf,” Saporito explained.
Retailers must share POS and loyalty data on a basis that is closer to real time, while manufacturers, who have access to data to make insights in real time, can create capabilities for building new, profitable growth platforms. “Benefitting from operational POS data requires tailored business streams, process capabilities, and technology enablers - to create value for retailers, manufacturers and shoppers,” Saporito said.
Using operational POS to drive collaborative growth
There are three primary areas where leading companies are profitably using operational POS data.
- Reduce OOS
OOS levels are a serious issue in CPG supply chains. Most retailers report OOS levels of 5-10% for base demand, increasing to 5-20% for promotional items.
One retailer and one beverage company faced this issue, so developed a pilot programme to counteract OOSs which were averaging 6.6%. By developing a predictive modelling capability that highlighted demand on a store-by-store basis, and on-hand inventories, a 32-day forecast was created, which translated into suggested orders - communicated to the local distributor and store personnel, so the entire value chain was operating on the same demand signal. As a result, OOSs were reduced from 6.6% to 3.4% - generating a 3% increase in product sales.
“By automating analysis at store level after every event, companies can improve their store-level planning and execution,” commented Saporito.
- Improve go-to-market vehicle performance
Most go-to-market events - promotions pricing, assortment, and packaging – target an entire retailer trading area, and are not tailored to events at the individual store. This can reduce return on investment for retailers and manufacturers.
“Utilizing operational POS data at the store level at a higher level of granularity exposes shopper insights that aggregated data might not,” Saporito stated. More detailed OOS data, which looks at sales by item, store, and day, will help identify differences among stores that appear to be comparable. Combining store level POS analysis and loyalty information allows for even more precise modelling of assortments, while layering in additional information about shoppers; like the contents of their shopping basket, provides a new level of precision to drive pricing, promotions and product design decisions.
- Improve in-store execution
Three quarters of all OOSs are caused by poor retail execution. Operational POS data enables new insights into which go-to-market events, which new items and which stores cause difficulties, so these issues can then be addressed - often in real time.
“Improved planning helps retailers match their promotional display size with anticipated customer demand to minimise OOS or excess inventories post-promotion,” stated Saporito. Better planning on its own doesn’t guarantee a successful promotion; but operational POS data can help identify retail locations that are noncompliant and rectify problems via an alert system.
Manufacturers are also using operational POS data for evaluating potential OOSs, compliance issues and deviations in predicted demand, via a field sales call.
Requirements for scaling operational POS Capabilities
A number of elements must be in place before companies can benefit from operational POS capabilities.
- Access to data
Manufacturers must negotiate with retailers to gain access to data. Both must be aware of the benefits of data sharing. A shared approach to analysis, insights, actions and measures can ensure that data sharing grows over time, as new users are discovered.
- Data collection and alignment
Data collection requires that item and location hierarchies are aligned between manufacturers and retailers and that the data recipient tests for completeness and quality. It requires manufacturers build and retain a data repository, or a demand signal repository, that can safely store and process data at required speeds.
- Data analysis and assessment
Analysis must be done in real time to facilitate real-time decisions. Traditional analytical methods are giving way to pattern recognition and the detection of exceptions, which are applied to the data when received. Timely data assessment allows manufacturers to distinguish between actionable insights and mere noise in the system.
- Fast decision making
Data and insights are of little value unless they can be acted on quickly. In most cases, new processes, roles and decision rights are required to enable an organization to respond at the speed required to leverage operational POS data.
Operational POS data is transforming how decisions are made today in the CPG industry. The challenges for implementing it are great, but market indications suggest the rewards are greater still. Regardless of the path an organisation takes to using it, the first challenge for operational POS, is to begin the journey.