With all the media noise about “big data” and artificial intelligence (AI), it’s easy to forget that data has been at the heart of retail success since well before the digital era. Keeping accurate records of sales, inventory, and product information is as old as handwriting, while product images are at least as old as photography. Digital computing did not create information technology per se; it merely moved that information from people’s heads and pieces of paper to complex code on a device. Data have always driven retail decisions; computers made the process faster and gave marketing directors more tools to plan and deliver campaigns. However, the tools can be problematic.

Case in point: Big retailers are virtually swimming (and sometimes drowning) in the detailed product information. Nearly all of this comes from the product manufacturers. The data are often incomplete, and the product data labels or metadata are inconsistent. Retailers must refine and house all this data in a sprawling product information management or PIM system.

Add another layer of data that portrays each product in the potential buyer’s imagination. Everything must be housed in another sprawling database, digital asset management or DAM system, from product photos and videos to detailed descriptions and reviews. This setup can be plagued with the same issues as a PIM system. Things can be further complicated by the need to have separate databases for inventory, pricing, customer feedback, and sales history.

The value of data-driven decisions

Having so much data gives marketing and advertising directors a considerable advantage. It provided the means of objectively knowing all there is to know about what products they should sell more of, why (and when and where) people buy them, and how best to advertise them across multiple regions and media channels. In practice, however, it is no simple matter. Reconciling and normalizing data from different product manufacturers is a huge task. Retailers need to do this and much more continuously.

Optimizing and using data usually requires a high level of automation. New product information and images are being created constantly and must flow smoothly and accurately into their respective PIM, DAM, and other database locations. All those data sources must also “speak” with each other, providing a complete picture of each product to those planning a campaign and putting that picture into every channel output.

Think of this complex data flow for retailers and manufacturers as a symphony orchestra. There are so many different instruments and qualities to consider. It can quickly become chaotic, but they all work together under a single conductor. The “music” they create can move the audience. In the case of retail, the audience is an incredibly diverse buying public. When they respond positively to all that orchestrated data, it means measurable growth and profitability.

Realising the value of data

While most retailers and manufacturers acknowledge the value of data-driven decisions, some may need more wherewithal to make it a practical reality. Retail has become more competitive and complex, and media channels have multiplied. In response, Comosoft’s LAGO system has emerged as the preeminent “conductor” for data-driven decision-making.

LAGO is uniquely positioned to leverage that complex data and effectively channel it into various well-planned, multichannel retail campaigns. This may include Comosoft’s PIM and DAM systems but can integrate smoothly with existing PIM, DAM, and other data sources as well. As described previously and in greater detail, LAGO uses its comprehensive data framework to inform the marketing decision-maker’s planning process. LAGO can do so even when marketing decisions must be made early and before the data are complete.

LAGO’s whiteboard capabilities allow marketing and advertising decision-makers to identify ideal products for promotion, place them in the best order, and transmit their decisions directly to their design and production teams. Once a campaign has been executed, LAGO enables them to judge its effectiveness and continuously improve their decision-making process.

The benefits of integrating PIM, DAM, and other complex data are seen in LAGO’s approach to marketing campaign design. Using LAGO’s innovative plugin for Adobe InDesign, production designers can access all the related data for a particular product without spending time hunting for it. If the PIM or DAM data are updated or modified, the information or images in the layout are updated automatically. That way, the designer can focus on what they do best—design a compelling display for the campaign.

LAGO can also quickly generate multiple regional versions of each marketing or advertising piece. Once the print version(s) are created, the resulting data can be automatically channeled to the retailer’s web and mobile app channels and even used for in-store displays.

Of course, integrating and optimizing all that mission-critical data is only the beginning. Comosoft has a long history of successful systems integration and database administration—all geared to empower better, data-driven decision-making. But still, more will be required as “big data” expands. Comosoft has recently partnered with retail AI developer DecaSIM to add artificial intelligence to LAGO’s already powerful toolset. In a recent case study, a large grocery chain used the combination of LAGO and DecaSIM’s AI-based model to enhance product selection in its weekly marketing campaigns, significantly increasing per-store sales results.

There is little doubt that mastery of one’s data is the “secret sauce” for retailers’ and manufacturer’s success in an increasingly competitive world. Making the right data-driven decisions will make all the difference. As it has been with structured PIM and DAM data, LAGO promises to be a leader when it comes to using artificial intelligence and “big data” to inform those decisions.

Find out more about Comosoft LAGO and our unique approach to data-driven, multichannel marketing. Or book a demo to see for yourself.