To reignite growth, consumer products companies need granular, integrated insights on commercial performance. And they need the ability to turn those insights into action, fast. Executives know this. That’s why the question we hear time and again isn’t, “Do we need analytics?” It’s “Why aren’t our analytics delivering more value and why is our ROI on data so low?”
The truth is most companies in the sector are failing to get full value from their data and analytics investments. That’s typically because they are not prioritizing their investment for today’s challenges or changing their working practices in ways that would enable them to create competitive advantage.
For example, the big make-or-break decisions in this industry are taken within arm’s reach of the shelf — virtual and real. It’s here that a shopper decides whether to buy a product. Yet we still see firms stuck in the old belief that periodic analysis at the 30,000-foot, macro level is going to tell them anything useful about this shopper’s behavior — never mind how to influence it.
It doesn’t matter how elegant your strategy sounds. If it’s informed by rudimentary analysis of dated, and incomplete information, its success or otherwise is more a matter of luck than judgment. The most likely outcome of this operating model is failure. And we’ve seen that happen repeatedly, at great expense, both in financial terms and in terms of lost market credibility.
So why doesn’t the clear desire to engage with commercial analytics deliver better results? We call it the “paradox of performance.” Every company has its own challenges, but essentially we see three points of failure that explain why many consumer products companies are not getting their expected return on investment from commercial analytics and data.
Three points of failure
1. Priorities are out of balance
Firms are spending too much on data and not enough on actionable business insights.
2. Execution lacks sophistication
Firms are using analytics in ways that are just too blunt and generic to shape consumer behavior.
3. Decision-making is rigid and slow
Firms are struggling to build analytics into decision systems that allow rapid action.
How to execute commercial analytics strategies that deliver value
To drive market performance, companies must be able to synthesize new data sources, apply advanced analytics and use those analytics when and where they make decisions. Yet many struggle with how to unlock their commercial performance. As a result, they cling to historical practices and dated approaches.
It’s time to move on. Our work with clients has identified five areas that will be essential in helping you transform the way your firm uses commercial analytics and data:
1. Integrate data and insight
To use commercial analytics effectively, firms need to be able to integrate different kinds of data from multiple different sources. Classic point-of-sale data is fine for reporting market share, but its relevance to decision-making is limited and shrinking fast.
Those that buy syndicated data are realizing it might be better to fuel their own or third-party decision platforms with disaggregated data feeds, combining structured and unstructured data at a fraction of the cost. Those savings can then be used to build new execution capabilities, driving data-investment ROI.
We have seen companies integrate direct-to-consumer, i.e., e-commerce, data and retail store data along with social sentiment insight with various econometric factors to unlock a better demand forecast. Not only did forecasting error get reduced, it also helped shape their channel strategies to be more responsive to changing consumer behaviors.
2. Scale analysis and insight development
Effective commercial analytics requires continuous insight across the organization. A one-off analysis or an ad hoc project can be useful, of course. But for real value, it’s critical that you create a consistent approach and a capability you can grow.
An important early step is to develop a common analytics “language” in your organization. When people are talking about a key measure, for example, there needs to be clear agreement about what they mean. Without that clarity, it becomes harder to drive adoption or to hold people accountable.
3. Create insights while you sleep
Firms must be able to spot changes in market sentiment and consumer behavior in real time — or even predict them before they happen. Then they can make rapid changes to pricing or media activity, for example.
Effective commercial analytics will generate insights that inform long-term strategy. But firms that only “call up the data” for a monthly or quarterly management meeting will struggle to get real value from their commercial analytics.
Invest in commercial analytics that are “always on” — always looking for and flagging opportunities for immediate action.
4. Translate insight into action
Firms too often underinvest in the skills they need to move from insights to real-world commercial decisions. One way to fix this is to build central commercial analytics teams around common business issues — such as managing revenue growth or making marketing more productive.
You can then invest in training and knowledge management to support and develop these teams. That will give you a core commercial analytics capability — one that combines data expertise with a detailed awareness of all the decision points that lead to winning propositions at the physical and virtual shelf.
5. Build analytics for consumption
It’s often in the gap between insight and action that the battle for consumer spends is won or lost. Decision-makers must be able to access analytics-driven insights whenever and wherever they need them. Increasingly, that won’t be when they’re sitting in front of a workstation.
Understand what questions commercial analytics can answer, how those insights fit into your business processes and who needs access to them. Then invest in commercial analytics that deliver the right insights to the right people in the way that works best for them.
The importance of people
The human side of analytics underpins each of these five areas. Our research suggests that many organizations are neglecting the “softer” capabilities needed to use analytics effectively, notably those relating to behavior.
Almost all automated processes require a human to make a business decision or change a business process, as a result of analytics. An organization that works to remove behavioral barriers is more likely to achieve full value from its analytics investments.