Improve your marketing efforts with predictive analytics

Marketing campaigns can be a massive undertaking with expenses to match. Tens of thousands of dollars spent on design, advertising, content, social media and more. Marketing can, however, be much more cost effective and compelling when predictive analytics are used to personalize the message and precisely control when and how it is delivered. 

What is predictive analytics?

Predictive analytics can be used to improve everything from software to healthcare and everything in between. They are very effectively put to use to benefit marketing efforts as well. Predictive analytics is the practice of analyzing statistics and facts obtained through data mining, algorithms, statistical models, predictive modeling and more to make educated forecasts about upcoming events or efforts.

A simple example of predictive analytics in action is a restaurant figuring out its staffing needs for upcoming shifts. The number of guests in a restaurant is dependent on a variety of factors, many of them having nothing directly to do with the restaurant itself (weather, nearby events, etc.). Using predictive analytics, models can be developed to ensure the restaurant is more precisely staffed, avoiding the expense of overstaffing and the nightmare of understaffing.

To help explain why predictive analytics is important, it’s vital to look at the goal of a marketing campaign itself: Ultimately, to bring in new customers, re-engage current customers and increase sales, exceeding revenue goals. To achieve those goals, it’s vital marketing campaigns be highly targeted and responsive, and supplies and inventory be available for any increase in demand. Predictive analytics can help tick all these boxes, but only if a business is working with the right data and knows exactly how to deploy it. 

What is retail analytics?

The power of predictive analytics in retail

Predictive analytics are particularly effective when applied to the retail industry. From supply chain and inventory management to customer demand and sales patterns, obtaining and analyzing precise data is critical to making cost effective, and successful expense and marketing decisions. 

Benefits of using predictive analytics in retail

If only a retailer could understand their customers’ behavior both in person and online – who is going to abandon their cart and why? What products could be upsold at checkout? How do buying patterns fluctuate during different life stages and throughout the customer journey? Retail data analytics  -- evaluating retail data to discover trends and other conclusions -- can uncover all of that and more.

Perhaps the easiest way to explain the benefits of predictive analytics in retail is to expand on several predictive analytics use cases in retail: 

Evaluating customer behavior 

Utilizing retail data analytics, retail leaders can lower customer acquisition costs, better understand channel usage, hone cross-selling and upselling opportunities, determine purchase intentions, pinpoint buying patterns as well as additional behaviors. Tracking and analyzing consumer behavior can pay off with customized marketing, personalizing in-store and digital experiences reduce cart abandonment and much more.

Customized experience 

With billions of products available in stores and online, retailers can rely on first- and third-party analytics in the retail industry to make accurate and compelling product recommendations. The customer experience and service can be further enhanced using retail analytics to gather and interpret data that helps predict the needs of customers, correctly fulfill those needs, anticipate and resolve issues and keep customers coming back for more.

Segmenting customers based on deep retail insights further allows for a smooth and outstanding customer experience. 

Focus on specific campaign targeting

Imagine if a retailer could create an offer customized to an individual customer, target customers based on events in their lives happening in real-time or segment marketing campaigns as specifically as “target only those people who have purchased these paper towels three times per month in the past four months.” It’s all possible with the use of big data analytics in retail.

What are examples of predictive analytics? 

• America’s top retail chain used retail analytics to predict when people will be shopping, what they will purchase and how they will choose to interact with the store or online experiences.
• A leading mattress company tested mover and pre-mover data to see when, during the moving process, consumers are most likely to buy a mattress.
• A top personalized shopping retailer using order update requests as opportunities for trend spotting and a chance to elevate product recommendations. 

Analytics in the retail industry are nothing new, but the amount of data available to retailers today is astonishing. And that data can be used to create powerful, authentic and personalized marketing experiences. 

Knowing how and when to stock your shelves 

Having the data to alert which items sell more quickly or slowly can save a significant amount of money. For example, utilizing data to make informed decisions allows for more accurate sales forecasting based on deep insights. From using seasonal demand to help lay a foundation for inventory ebb and flow to predicting potential supply chain disruptions based on major human or weather events, predictive analytics can help retail businesses finely hone inventory knowledge and actions. 

Improved targeted strategy for future campaigns 

The more data available, the more information a retailer has to form a highly targeted approach for future marketing campaigns. Utilizing data-driven marketing can provide a full view of all consumers and even other businesses in the retail market. Specific data assets can help identify customer pain points and opportunities to execute strategic marketing quickly as well as flag challenges so the path to purchase can be smoothed over.

When retailers work with a data partner like Deluxe, they not only benefit from decades of experience, but they also have access to some of the most up-to-the-minute, actionable data from basic demographics to geographic and life event trigger data. In fact, trigger events provide some of the richest marketing opportunities for retailers.

Using predictive analytics, retailers can identify customers before, during and immediately after major life milestones such as getting married, having a baby, purchasing a home, selling a business, applying for a loan, and much more. The right data can help retailers get customized marketing campaigns in front of the right consumers at just the right time.  

Predictive analytics can transform your retail business 

Whether a retailer is just wading into the world of predictive analytics or is ready to take their marketing game to the next level, many are left wondering just how to implement predictive analytics.

It’s incredibly difficult for most retailers to obtain the sheer volume of data required for effective predictive analytics. Beyond that, aggregating multiple datasets, identifying high- and low-performing suppliers, and activating the data rapidly is outside of the realm of what most retailers could (or would want to) tackle.

The most important element is to work with a seasoned data-driven marketing partner, such as Deluxe, which can work with retailers of any size on campaigns that are both broad and in-depth. Some of the areas Deluxe specializes in include: 

• Strategic campaign design including customized strategies, performance forecasting and cross-channel activation.
• Data integration and audience selection utilizing proprietary, multi-sourced data and decision engines to select just the right consumers for the right marketing messages.
• Creative strategy and development to help design personalized and differentiated marketing experiences.
• Campaign production and deployment across direct response marketing channels as well as sequencing and optimizing channel activations.
• Tracking, measurement and optimization so retailers always understand marketing results and return on investment.

Actionable data insights optimize marketing efforts. In a retail landscape that is more competitive than ever before, predictive analytics offer the cost-effective competitive edge businesses need to become a customer’s first choice and inspire loyalty.