5 Ways Omnichannel Retailers Leave Money On the Table
Ever since “omnichannel” became the buzzword among retailers, brands have focused their efforts on understanding their customers’ data to increase engagement and profits. The move is paying off. Brands that prioritize omnichannel engagement strategies retain, on average, 89 percent of their customers. Given this, retailers have increased technology spending and prioritized omnichannel strategies to remain relevant.
However, getting omnichannel “right” isn't as easy as it seems. Despite making significant progress, retailers are still leaving money on the table. Here are the top five mistakes that prevent retailers from improving their long-term return on investment:
1. Ignoring predictive modeling: Before predictive analytics, brands relied on historical data to make educated guesses about what to do in the future. While the analysis of past data is an integral part of any omnichannel strategy, it's now only a small piece of an intricate and complex puzzle. Today, retailers have access to more and a larger variety of data from a single point of interaction. For instance, customers are making decisions based on emotion — how they feel about a brand, product or product description — as well as personalized recommendations, promotions, urgency and a host of other factors. By using predictive data models, retailers can build context around customer data to predict meaningful outcomes.
This means retailers can infer which products will sell out faster based on how certain words or images resonate with consumers within a product description. An effective predictive data model searches for meaningful relationships between historical data and future outcomes in order to predict short- and long-term customer behavior. Ignoring predictive analytics and relying solely on dashboard tools and educated guesses will compromise a retailer’s ability to engage shoppers and get them on a path to conversion.
2. Hiring the wrong data “experts”: Hiring the wrong talent is a costly mistake for any organization, but it’s particularly troubling for retailers that mistakenly hire one “expert” to fulfill the roles of many (data engineer, data scientist, IT). While data engineers build products that make data usable, data scientists derive value from that data to meet broader corporate objectives and identify trends. Furthermore, data scientists hold a unique set of mathematical and statistical skills that enable them to solve complex challenges in an omnichannel environment, something that a qualified data engineer lacks. Even brands that understand this distinction have a difficult time filling these roles due to a shortage of qualified data scientists. However, putting the right people in the positions that maximize their expertise will enable retailers to make data-driven decisions and positively impact their business.
3: Prioritizing the wrong types of data points: Retailers have access to a significant amount of customer data points, but in order to succeed as an omnichannel business, they must prioritize their data tracking and analysis and thoughtfully select which data sets will create the most value. By tracking event-stream, user and feedback data, retailers can create more effective strategies to attract, engage and retain customers. For instance, a data scientist might examine why a loyal customer isn't coming back by looking at these three sources of data. Or he or she might determine a customer’s lifetime value to predict where the customer will be in the future and understand what’s driving him or her there.
4. Focusing on profit rather than experience: IDC research shows that omnichannel consumers spend an average of 15 percent to 30 percent more than their counterparts. While every retailer is focused on profits, it’s customer experience that drives consumers down the path to conversion. Leveraging data strategically can improve experience, conversion and revenue. In fact, McKinsey & Company predicts big data has the potential to increase retailers’ margins by 60 percent when used properly. A data scientist, for example, can see and track customers’ interactions across various touchpoints and learn which actions will have the highest ROI. Brands must look at past, present and future data points to extract meaningful trends and use that to create an engaging customer experience. The better the experience, the more credibility and trust brands build with their customers, which ultimately drives ROI.
5. Investing in the wrong areas: As critical as front-end tools and dashboards are for a brand's longevity in an omnichannel world, so too are the inner workings on the back end. Investing in a data warehouse, the right tools and people who will clean and wrangle data is essential. Big data insights are only as valuable as the quality of data being used to create that insight. ROI may come slowly at first, but investment in the right areas is sure to pay off.
In fact, retailers experience meaningful impact over the the course of one year by leveraging a combination of human and machine intelligence. Not only must retailers understand how to keep good customers, they must also apply data science to attract loyal ones. The value of that data grows at an exponential rate. As the amount of data grows, so do the insights gleaned from it. Early-stage companies may start with basic data mining and analytics, but as soon as they incorporate predictive analytics they can experience long-term success.
By 2018, Gartner predicts that more than half of all large organizations will compete using advanced analytics and proprietary algorithms. Now is the time for brands to improve upon what they’ve already built and start integrating data science, skilled teams and predictive modeling into the business.
Jonathan Beckhardt is the co-founder and general manager, insights, DataScience, a company that combines human intellect with machine-powered analysis to create simple, actionable insights from complex data.
