Use AI-powered customer data to get every team in your organization aligned around the same goal: delivering the optimal customer experience.
Since the digital revolution took hold, competition in the retail space has become fiercer than ever. In this infinitely crowded marketplace where brands are fighting for the attention of a finite pool of consumers, customers have elevated their expectations of retailers, demanding competitive prices, higher quality goods, and a wider range of inventory — not to mention brand transparency, the application of cutting-edge technology, and speedy delivery times for e-commerce sellers.
At the same time, consumer tastes are changing more quickly than ever. In fact, according to Shopper-First Retailing report, 59% of e-commerce bestsellers change on a monthly basis. That’s a lot of balls to keep in the air, and every team — marketing, finance, IT, merchandising, business intelligence, and executives alike — is part of the juggling act.
Making Every Team Customer-Obsessed
In a retail marketplace flooded with competition and shifting preferences, customers aren’t brand-loyal anymore — they’re experience-loyal. According to a Walker study, the customer experience will overtake price and product as the key brand differentiator by 2020. To pull out ahead of the competition, brands need to be placing the customer experience above all else.
While different teams face different challenges, they all ultimately need to be working toward this goal — and, of course, using the same set of data to do it. That means breaking down silos so that all teams can focus on a customer-centric approach.
But while access to unified data is important, it’s only half of the equation. We’ve entered a new era in customer analytics: rather than explaining what happened in the past, AI and predictive analytics technologies are shifting to provide an understanding of why it happened, what to do about it, and even what’s going to happen in the future. The very best analytics tools help every team in an organization apply practical data-driven insights that boost revenue in the short term and, more importantly, drive long-term growth.
No Team Left Behind
Here’s how AI-powered predictive analytics can help every team in your retail organization drive greater value for the organization from the top down.
With the help of AI-powered insights, your organization’s executives can keep a finger on the pulse of core KPIs, gain clearer insight into real-time client health, understand the underlying drivers of revenue growth, and measure the impact of big initiatives like expanding into a new geographic area or engaging millennial shoppers. In short, access to better customer analytics equips executives to lead their organizations to long-term, sustainable growth.
The core function of finance teams is centered around data, and AI-powered models make finance teams run more efficiently than ever before. In most organizations, the office of the CFO will consider macroeconomic conditions, retail industry statistics, and the organization’s historic performance to help predict what an upcoming quarter will look like. With AI, this team can look ahead instead of behind, forecasting earnings on the customer level — even for customers that haven’t yet been acquired.
When you think of the teams that have the most to gain from customer-centric, AI-generated data, the marketing department is likely at the top of your list. Predictive analytics tools can be directly put to use for email marketing campaigns, search and social marketing, display advertising, and more. Offering more precise customer segmentation and customer profiles, AI empowers marketing teams to target consumers with the products they want, when they want them, on the channels that are most likely to grab their attention.
IT and Business Intelligence:
A sophisticated predictive analytics platform can provide near-instant answers to questions that used to take IT teams days or weeks to dig into. In other words, AI-powered insights take the grunt work out of the IT and BI teams’ jobs, freeing them up to focus their attention on higher-level projects. That means those projects get done more quickly and more effectively.
Back in the old days, merchandising teams’ approach to predicting future demand was a bit unscientific: factors like how many of a given product they’d sold in the past and how well the business was doing as a whole would factor into purchasing decisions, but the whole thing was a bit of a flying-by-the-seat-of-your-pants endeavor. When AI-powered customer data burst onto the scene, things got more interesting. Now merchandising teams could dig into the trends and uncover useful insights: maybe revenue was up overall, for example, but women’s sales were flatlining while men’s sales were shooting up. With insights like these, merchandising teams are equipped to make more precise decisions about what products to buy and when.