The Consumer Packaged Goods industry today is in the midst of an interesting bundle of challenges and opportunities. While rapidly changing demographics and consumption choices in most markets and the associated complexity across the value chain is a challenge, rise of e-commerce (despite what might look like a threat from an “A” player) and the growing adoption of analytics-driven decision making by business teams are clear opportunities.
In our view, smart use of data and analytics – ranging from relatively simple exploratory data analysis to advanced DS/ML/AI models that deliver actionable insights in five key areas can help CPG manufacturers improve their growth and competitiveness: E-commerce, Trade Spend Effectiveness, Pricing, Marketing Effectiveness, and Responsive Manufacturing & Supply Chain.
In a series of posts over the next few months, starting with this overview, CPG industry practitioners from Tiger Analytics will talk about current best practices in data management and analytics around these five areas:
1. E-commerce – For most CPG manufacturers, a significant part of revenue growth (if not all) in the next few years is expected to be from e-commerce. Surprisingly, this is also a channel that is not very well covered by the traditional data providers. Pioneers in the industry are adopting unique approaches to acquire, integrate & analyze e-commerce data that puts them ahead of the game. If you are wondering about Direct-to-Consumer (DTC), see point 4 below.
2. Trade Spend Effectiveness – Various studies conducted over time show that return on trade spend, which is either the first or second highest spend often running into billions of dollars, is negative for most of the CPG manufacturers in the industry. While robust trade promotions management (operational aspects of defining promotional calendar, promo execution & reimbursements) is in place across the board, analyzing the effectiveness of spend and establishing ‘true incrementality’ of trade spends is an area of big concern. Here again, CPGs that win are going beyond toolsets offering a siloed view of incremental volumes (i.e. just one retailer at a time) to get a full view of effectiveness based on comprehensive data and advanced analytics.
3. Pricing – While promotions look at the effect of temporary trade events, getting everyday prices right is equally important, if not more, since a significant portion of CPG manufacturer trade spend goes into maintaining long-term price gaps warranted by competition, or by retail partner demands. While most CPG manufacturers make pricing decisions at an item/item group X channel level, winning CPGs are making these decisions at a much more granular level, here again taking advantage of data & analytics.
4. Marketing Effectiveness – Marketing spend, which is a close counterpart of trade spend is another top spend item on a CPG manufacturer’s P&L. Two key sources of data & insights have been found to improve effectiveness:
a. Consumer insights generated using data from consumer panels, shopper cards, social media, brand page user registrations, and other Direct-To-Consumer* initiatives help get a clear understanding of consumer preferences and decision hierarchies
b. Insights around traditional and digital media channels (in the form of transparent media mix models) and their impact on short and long-term brand objectives
Of all areas, this is probably the most investment intensive from a data & analytic infrastructure perspective when CPG manufacturers want to go beyond a siloed view (often lamented as coming from agency delivered black-box models, or from the ‘walled gardens’ of the digital world). However, the pay-offs for initiatives are worth the effort, especially for larger brands with higher marketing spends to deliver returns on.
5. Responsive Manufacturing & Supply Chain – compared to other areas, manufacturing & supply chain functions of CPGs have the need to plan over multiple time horizons, across all of which data and analytics play an important role.
a. Near-term (up-to 3months): analyzing out-of-stock and fill rate for maintaining retail partner service levels, and impact of transportation partner & lane decisions are relevant in this time horizon.
b. Mid-term (>3 to 12/18months): understanding impact of promotion plans on shipment volumes over and above the baseline demand picture provided by the Demand Planning function is important in this timeframe, to plan and adjust mid-range production schedules without much of incremental capacity.
c. Long-term (>12/18months – esp. for manufacturing plant and warehouse capacity planning): SKU rationalization – a touchy topic becomes feasible to address in this timeframe due to the amount of internal and external change management it takes to move on this, more than the time taken for analytics.
Interestingly, this is also a space that is witnessing a higher use of Robotics (automated inventory management and warehousing).
While it may appear addressing all the five areas may require significant upfront investments in integrated data environments (DSRs), our experience shows having a clear focus on leveraging analytics to generate specific, actionable insights could help realize significant business value immediately, even on current state data & analytics environments – wherever in the maturity curve they are.
Stay tuned to hear more from us in this series.
Note* Direct-to-consumer initiatives of most CPG manufacturers are often more valuable as a source of augmenting rich consumer insights than being a significant channel of sales volume. Exceptions being brands with significant direct-to-consumer sales, such as lifestyle/sportswear brands and health-beauty-cosmetics manufacturers extending their consumer reach through their own salons.
Also, check out our other blogs on CPG Analytics:Consumer Insights CPG Analytics CPG Analytics Trends