Now more than ever, vendors must focus on delivering value. To do so, they simply cannot continue to push product features or even perceived customer benefits – instead they need to clearly communicate the value that their solutions deliver to their customers, their customers’ customers, and even further down the customer chain.

Data Analytics – a prime example

Data Analytics is a clear example of a supremely beneficial technology – for enterprise customers. It can highlight organisational, market or industry trends and reveal competitive threats or opportunities that would otherwise have gone unnoticed.

And yet, vendors have not succeeded in communicating the value to end-consumers. Instead, vendors have sold technology and features such as mobility, self-service and collaboration, presenting these as solutions to common enterprise customer problems.

Feature fail

Why doesn’t feature selling work?

One reason is that many vendors compete around the same product ‘differentiators’, leaving little to distinguish them. The flashy new features they’ve added in the latest release will only be unique until the next release/upgrade from the other vendors as everyone is playing catch-up quickly.

Another is that customers today know more about the products they want and their suppliers than ever before. The Internet has made it that much harder to compete on features alone.

Levelling up

It’s not hard to make a case for focusing on value at a time like the present. If vendors cannot motivate how using their products will inspire a buying decision in consumers, enterprises simply won’t see the point.

But what does Data Analytics value look like in action? A few examples should suffice:

• Analysing customer data can help companies to build the most common consumer personas, allowing them to construct key marketing messages that address common pain points. The value is in understanding consumers better in order to deliver clearer value, based on actual preferences.
• By analysing sales data, incongruities can come to light, such as a disparity between revenue generated by high-volume, low-price items and more high-end items that move slower but drive up cross-selling revenue. The value is in identifying what product combinations consumers value most and delivering more of it.
• Looking closely at its supply chain, a company will be able to pinpoint areas of delay, including specific products and transport methods that are prone to delay. The value is in addressing specific delays to overcome customer disappointment.

Next steps

In turning to value-based selling, vendors must reassess their go-to-market strategies, taking care to map delivery to ultimate consumer needs.

They must work to come to a clear understanding of customer business processes and applications, and how they can add value to their customers’ customers.

The value of Data Analytics must be tied closely and unmistakably to end-customer value. This requires a lot more than normal in-depth knowledge and understanding of the customer’s business and their value proposition. Without such extended understanding, how can the vendor possibly work with the customer to unlock the value in the data?