Deals and promotional offers are critical drivers of retail sales, especially among millennials. New ways exist to eliminate the guesswork in offer planning.

 Shoppers love special offers. They’re a financial release valve in uncertain, inflationary times, but the positives go beyond spending and budgets. A 2015 study found that receiving an online coupon produced the same neurochemical response as having a positive social interaction.

Warm feelings are just the start. Special offers have a substantial influence on consumer behavior, improving customer satisfaction, establishing deeper connections, and increasing profits. In a 2018 survey from RetailMeNot, four out of five respondents said that finding a good offer or discount is on their minds throughout the purchasing process. And a whopping ninety-three percent of all respondents said they would make a repeat purchase with a retailer that offered good discounts.

The importance of offers increases when retailers consider the next big wave of consumers—millennials. In the same survey, seven in ten respondents aged eighteen to thirty-four said they could not complete a purchase without first searching for a deal or offer. Eight out of nine said that finding an offer for a brand or retailer that was new to them would encourage a first-time purchase.

Data Overload

So, if deals and promotional offers are mission-critical, why do marketing directors and merchandising planners need help implementing the right ones? The answer is that today, there is simply too much data. The sheer volume of product and purchase data alone makes it impossible to plan the ideal offer manually.

Take product data. For each of the thousands of products a large retailer has to manage, there are hundreds, if not thousands, of individual data points—from product information stored in a PIM to images and other assets stored in a DAM to other data systems for pricing and inventory.

Ideally, all these data sources are highly structured and interconnected. But even with well-structured data, planning the ideal special offer out of millions of data points is a classic needle-in-the-haystack problem. The only option left is to trust in your gut.

The other side of the data problem involves customer purchase behavior. Some of it is structured, especially if purchases are made through a loyalty program, but much of it is unstructured in the form of customer feedback and product reviews. This raises a new problem. Much of this data comes from our smart devices—channels for marketing campaigns and special offers. Advertising and marketing directors can potentially leverage those channels but often don’t have the time or resources to create customized, individualized offers.

Step 1 – Fix the Data Logjam

  The first step in resolving the PIM/DAM problem is to ensure data consistency across a retailer’s marketing operations. This is a challenge, as much of the data comes from manufacturers who may have different names for data fields or missing information altogether. Fortunately, systems like LAGO (and our database administration services) can help retailers achieve a “single source of truth” across all their data sources. Fortunately, systems like LAGO (and our database administration services) can help retailers achieve a “single source of truth” across all their data sources.

Once the product data and assets are unified, the retailer can ensure that every promotional campaign element is consistent and error-free across every media channel. This must be the case with print catalogs, even when multiple versions are needed to meet geographic or demographic marketing needs. However, it is imperative for the many different digital publishing channels. Creating accurate special offers will be time-consuming without a consistent, multichannel marketing platform.

Step 2 – Create Personalized Experiences

  The rise of mobile and e-commerce systems is both a problem and an opportunity for retailers. Mobile devices are an increasingly powerful, two-way channel for finding, buying, and talking about products, whether at home or in the store. Used thoughtfully and with regard to customer privacy, the resulting data can be used to create personalized messaging, provide recommendations, and offer special pricing and promotions on regularly purchased products.

Customer data is invaluable in creating personalized, one-to-one communication, or “direct individualized marketing.” Such a system combines known behavior and preferences with relevant product data and images. It can then provide consistent and error-free output to print and digital channels. The individualized messaging and any included special offers or promotions (also tailored to the customer) can be refined over time based on subsequent purchases.

Step 3 – Make the Data Work For You

In the past, planning just the right special offer has been a matter of intuition and a bit of good luck. But even when merchandising planners are highly skilled at creating an appealing offer, there are not enough hours in the day to plan those offers across so many channels and with so many products to choose from.

Fortunately, the flood of available data, properly handled, can be the source of the solution. Comosoft’s collaborative workflow allows campaign planners, creatives, and decision-makers to work together, using a virtual whiteboard to visualize the page and select the products best suited to the campaign and its featured special offers. The unified PIM and DAM data support the visualization, streamlining the choices based on pricing, margin, inventory, and other vital factors. The results are automatically conveyed to the design and production departments for print and digital channel output. But that is only the beginning.

Artificial intelligence (AI) can use data of any kind to train a system to recognize meaningful patterns and recommend action—by human decision-makers or on its own. In a recent case study, AI effectively planned and optimized retail promotions. Using training data from Comosoft LAGO and an AI tool from DecaSIM, a retail grocery chain increased customer engagement with their advertising—boosting sales and increasing earnings by approximately eight percent. The AI tool detected meaningful shopping patterns and, by feeding the conclusions back to LAGO, identified related products for special promotion during the campaign.

Offer and merchandise planning need not be a “shot in the dark” ever again. By analyzing customers’ general and regional purchase behavior and their response to previous offers, an AI model can guide retail marketers through a sea of data to a reliable means of planning effective, loyalty-building offers.

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The number one goal for retail advertising and marketing directors today is the same as for decades—creating an engaging, satisfying customer experience to build lasting loyalty. Special offers and promotions have always been vital to meeting that goal. With advanced data visualization and AI tools at our disposal, planning and executing those offers has never been easier.

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Find out more about Comosoft LAGO and its potential to transform your retail data and marketing strategy. Or book a demo to see for yourself.