Retailers must leverage both online and offline data to deliver a purpose-built customer experience.

FREMONT, CA: Making data-driven decisions that are more accurate and timely is critical for retailers. The retail industry's competition is becoming increasingly fierce, and the omnichannel landscape has exacerbated the pressures retailers face. Ecommerce has spawned new initiatives for digital transformation, accelerating the growth of data. By 2023, 58 percent of retail sales will be impacted by digital. Possessing a goldmine of sales and customer data is one thing; fully exploiting it is another. Unfortunately, many organizations struggle with accessing and analyzing large, complex data sets. Indeed, only 13 percent of organizations claim to be maximizing the value of their available customer data. Three reasons why retailers struggle to maximize their data are as follows:

1. Business users frequently lack access to big data

Numerous existing business intelligence solutions require advanced SQL skills, forcing retail teams to field data queries from experts. This creates bottlenecks and time delays, impairing retailers' ability to deliver exceptional customer experiences. For instance, when it comes to inventory management, shoppers expect to purchase the products they want, when they want, and through the preferred channels. Still, reliance on specialized analysts impedes meeting customer needs quickly.

2. Traditional spreadsheets are incapable of handling large amounts of data

Many retail teams continue to rely on traditional spreadsheets for all activities. Still, they do not scale for big data—the more data, the slower the spreadsheet processes, increasing the likelihood of data corruption and making it nearly impossible to glean valuable insights.

3. Extracting, relocating, and combining data is time-consuming and introduces the risk of insufficient data

The time spent by teams on non-value-added tasks such as manual data collection, consolidation, verification, and formatting leaves little time for the type of analysis and strategic planning required by retailers to convert data to dollars.

Additionally, these workflows introduce the risk of data manipulation by accident and stale data. Incorrect data results in inaccurate forecasting and faulty decision-making, both of which can have significant financial consequences. Additionally, inaccuracy affects the ability to provide personalized customer experiences.

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