In this special guest feature, Julie Lockner, Global Market and Partner Programs, Data Platforms at InterSystems, discusses why businesses don’t need to stop investing in big data, but better manage and analyze the data in their arsenal to provide more personalized customer experiences. Julie leads global data platform and partner marketing programs for InterSystems. She has more than 20 years of experience in IT product marketing management and technology strategy, including roles at Informatica and EMC.
Effective business leaders are data obsessed, but often that is offset by a healthy dose of intuition. They know their business, and they know their market. They understand how data can be used to validate a hypothesis, identify anomalies in trends, and make adjustments in their business models to act quickly when a competitive advantage presents itself. These leaders have structured their organizations with disciplined data governance and continually invest in data management and analytics capabilities. Through both structured learning and real-time feedback loops, they are continuously looking for opportunities to use data-derived insights to move ahead in their markets.
According to a Gartner survey of technology executives, 48 percent of companies invested in big data in 2016, up three percent from 2015. However, the proportion of companies who plan to invest in big data within the next two years fell from 31 percent to 25 percent over the same time period. Was it because other executives that made large bets on big data were not able to show a return? Or was it because they made the capital expense for the technology and now expect teams to execute? My money is on the latter.
Businesses need to continue investing in their data management capabilities to ensure their return, and often incorporate a detailed data model to support the investigation and tracking of their problem-solving activities. The most successful management projects are those with a very clear vision on what problem the firm is trying to solve – and that problem is either validated by existing data or is a problem because of the lack of data. Sustainable solutions to problems build a continuous feedback loop into the new work design. The feedback loop is only as effective as the quality of the data used to measure progress.
Now that the budgets have been spent on the technology and the big data tools are in place, what problem are you trying to solve?
One problem that is likely to be at the top of your priority list is improving customer experience in today’s on-demand economy. Consider what you can improve. For example, do you really know how well your organization is creating that personalized customer experience at every touch point? Do you know why customers leave? Organizations that do not have a handle on how customers are engaging with employees will be left with competing on price – yielding smaller margins, lower customer retention rates, and potentially mismatched product marketing strategies. Something like a thoughtful lifetime-value calculation can quickly tell you how much customer retention is actually impacting your future cash flows and justify why this problem needs to be solved.
Let’s use the example of a retailer whose target customer is a young adult between the ages of 22 and 35. This customer generates $100 net revenue with a historic retention rate of 60 percent. Let’s assume the cost of capital is 4 percent. The marketing department spends roughly $50 to acquire the customer. Improving your customer retention rate by 5 percent yields an extra $29, or 16 percent increase in lifetime value. Lowering the cost of customer acquisition or improving net revenue also plays a role. However, customer retention helps sustain a business.
By quantifying the value of improving this retailer’s customer experience, retailers can now start to look at every touch point and optimize the customer journey. This now brings focus to the data and questions what you really know about your customer.
If your organization can access data sources in real-time while customers are interacting with your website or in-store agents, you can implement a new engagement model that optimizes their experience to minimize attrition while maximizing net revenue. Systems such as point of sale, loyalty programs and inventory management now become vital sources of information that can be used to tweak the customer experience, yielding incredible returns. The only thing left is to design the data workflow that combines data from transaction processing systems with real time analytics, and impress the customer with the best experience.
In today’s evolving customer landscape, investing in big data is a must for companies that want to maintain a competitive edge. As we continue to see advancements in data analysis and management, companies should be doing all they can to stay ahead – they just need to know what problem they are trying to solve and what data they need to solve it.
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