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‘Optimizing Growth’ with big data and predictive algorithms

growthFor your business to succeed, it needs to grow. But growth these days is harder than it has ever been.

Jason Green, Mark Henneman and Dimitar Antov aim to show readers the way forward with new growth strategies made for a customer-centric market, if you’re willing to embrace big data and move to a model that is built around attracting, motivating and retaining the most profitable customers, which they call a “demand-based business system.”

In “Optimizing Growth,” the authors argue that while it may be a lofty goal to try to build something that tells you deeper insights about want customers want, tailors offers to meet customers’ demands and expectations and catches customers when they’re ready to buy, that model already exists, and your business needs to adopt it for sustainable growth.

The first of the book’s three sections makes a case for why greater precision is necessary in business analytics. Because many traditional approaches to growth aren’t as successful as they used to be, a newer, more precise business model can drive profitable growth. Demand is more fragmented than it has been in the past, and growth can be slow, so businesses should move to a “demand-based business system.” Moving to such a system can also optimize costs, because it helps find waste that is not aligned with profitable demand, the authors argue.

The next section, about half of the book, is about how to use precision to optimize your business, and the last section explains how big data can help your business move to a demand-driven system.

To illustrate the power of removing internal data silos and improving customer-centricity throughout a business, the authors use a case study about how they developed an “enhanced demand landscape” — which better identifies customer segments —  for an insurance company after the financial crisis of 2007 to 2009.

They began with a custom survey on a representative sample of their internal customer database. They broke the database into six segments that could be differentiated on demographics and insurance usage and self-reported attitudes and motivations from the survey. Then they added more data about the customers from inside the company and supplemented that with data from outside vendors to provide even more information about how to identify each customer segment.

Next, they developed a predictive algorithm to replicate the segments in other places, and updated the internal customer relationship management database so each customer was placed in one of the segments. With that information, they were able to learn that the business had been disproportionately losing customers from its most preferred segment.

The company began developing a targeted retention strategy, using technology to predict the likelihood of a customer leaving, and improving customer interaction.

The company also learned that 60% of its insurance agents underperformed expectations, and while some of those were serving customers from unfavorable market segments, others were serving highly favorable segments. The company began programs to improve site merchandising based on the composition of customers in the area, with the aim of driving some customers to the agent and others to a call center, depending on which customer segment they had been assigned. They also taught agents and call center representatives how to identify customers’ most likely needs and to use a script based on the customer’s segment.

The result: The business had a 58% improvement in customer retention, and new customer acquisitions represented a higher part of the desired target segments.

Wrangling data and wielding predictive algorithms may seem like a tough nut to crack, but if you can figure it out, the authors of “Optimizing Growth” argue you’ll have a significant benefit over your competition, because you will be better at finding the best customers and closing sales with them. At 225 pages, it’s not a light read, but its use of boxes with thought-provoking questions can turn reading the book into more of an educational experience.

Optimizing Growth

Predictive and Profitable Strategies to Understand Demand and Outsmart Your Competitors
By Jason Green, Mark Henneman and Dimitar Antov
Wiley. 225 pages. $28.

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