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10 Mistakes to Avoid to Better Leverage Retail Analytics and Forecasting

Heading into the 2019 holiday retail season we are all examining ways to better leverage retail analytics and forecasting strategies. Smart retailers are making technology investments that help them analyze the right data and gain insights quickly to show fast ROI. They can leverage external leading indicators to foresee headwinds and plan accurately for the future. Consumer sentiment and POS data can be used by brick-and-mortar and online retailers to understand the specific actions people take in stores and adapt the shopping experience accordingly.

Retailers can more effectively create marketing campaigns, optimize supply chain and staffing decisions, and ultimately increasing revenue while decreasing costs. However, companies often jump on the data bandwagon without thoroughly evaluating their data strategies.

Retailers need to carefully consider exactly what retail analytics data they plan to collect, how they will evaluate it and how they will act on the results. To help companies approach analytics more efficiently, we have compiled a list of the most common mistakes we see retailers make when trying to incorporate retails analytics into their business processes.

Top 10 Retail Analytics Mistakes to Avoid

  1. Trying to boil the ocean – The possible data sets a retailer can use are virtually endless. From customer behaviors and internal performance metrics to third-party industry statistics/trends from companies like Nielsen to open data available on economies worldwide – companies that try to gain meaningful insights from ALL of the data available are destined to fail. Instead, focus solely on the key metrics that matter most to your business.
  2. Ignoring leading indicators – Speaking of the metrics that matter to your business, those that tell you what happened in the past can certainly help you add context to previous performance. However, they do nothing to help you plan for the future. Instead, prioritize the data sets that help predict your future performance.Retail Analytics and forecasting
  3. Creating single-use, point-in-time data models – Many current statistical modeling methods are out of date as soon as they are developed. Designing data models that paint a picture of a specific point in time can’t help you plan. Instead, create models that are automatically updated as leading indicators and key performance metrics change.
  4. Not aligning with the company’s data analytics strategy – At this point, retailers know that they should be using data. However, incorporating data analytics into business processes requires more than just collecting and reviewing data. The time spent on these efforts is wasted if business units don’t understand the methodology or do feel that insights from the data team are not timely enough.
  5. Ignoring what the data says – Because nearly half of executives are not aligned with their data strategy, too often, executives rely on gut feel. Research is showing that this statistically leads to decisions with poor outcomes. With consumer preferences changing rapidly in response to e-commerce, wearables, and potentially soon, VR, there is no better time to incorporate analytics to prepare for these changing dynamics.
  6. Limiting data use to a single decision/business unit – Predictive retail analytics has benefits throughout an organization, but often its adoption is siloed, and these benefits aren’t shared. Likewise, data may be used to focus on one key performance metric – like overall sales, but SKU-level or market-level variations are ignored.
  7. Focusing on single-channel data sets – Similarly, we often see silos in the data sets used. In-store metrics aren’t married to e-commerce. Mobile shopping habits are siloed off for a mobile team to analyze. Instead, these metrics should be integrated for a true understanding of trends.
  8. Treating all data as equal – Not all data is quality data. It’s essential for retailers to thoroughly examine the credibility of their data sources and cleanliness of their data sets or risk making decisions on outdated or erroneous metrics.
  9. Focusing on the individual alone – Data on individual preferences can be hugely helpful in sales and marketing processes, allowing retailers to hone in on precisely what a customer is most likely to buy, and when. But if retailers don’t bubble up these metrics to see bigger trends, they are missing the opportunity to make smarter, data-informed decisions.
  10. Not communicating the results – Retailers can’t act on data analytics if they don’t know what the data says. Communicate findings to all of the teams that need the data to make better decisions; it is the only way that retailers can create meaningful changes based on the results.

The Retail Analytics and Forecasting Path Forward

By identifying areas of strengths and weaknesses in your data organization, and by working with the right analytics partners to assist in those areas, you can gain ample time to educate business leaders on the methodology used, gain consensus on the findings, develop a plan, and then take action with the right leaders’ support.  In fact, getting team support may be the easy job, it may well be that the most significant barrier to entry right is how to integrate the right technology solution to becomes a truly data-driven organization.

In the “2019 Executive Survey” report findings amongst C-Level and senior executives, the top obstacle for efficient and accurate planning, according to the respondents, is not having the right software to collect and synthesize the data (46%). However, even if they have the right data, the second biggest obstacle (42%) is turning that data into actionable insights. Many respondents also noted that accessing internal data is still a major challenge (37%).

According to a recent NewVantage Report 2019 Big Data and AI Executive Survey, a majority (69%) of global retail, CPG and other industry executives readily admit that they have yet to create a data-driven organization. Further, executives that identify their firms as being data-driven has dropped from 37% in 2017 to 31% in 2019. So, why is the evolving role of data and analytics posing problems for teams? Based on findings, leaderships reservations on the importance of data is less the issue than decoding the myriad of emerging technology and perhaps how that technology fits within their organizations to efficiently convert data into insights.

What we can all agree on is that now, more than ever, retailers are faced with an unchartered territory that requires untried and robust technology and analytical power.  This effort takes and the entire team. Most business leaders realize that data and analytics improve business operations and the bottom line. From improving planning processes to enhancing customer relationships and mitigating risk, there is an increased desire to operationalize insights gleaned from data. However, tapping into its true potential comes with challenges. In order for retailers to stand out among the crowd, they will need to pivot toward a data-driven culture and high-level planning that will bring together the right economic intelligence, right data, and the right software.

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Companies should be leveraging technology solutions that not only auto collect, clean, and organize data, but also identifies external factors that will impact demand for their unique products, solutions, and market needs. Over 40% of executives said that having a third-party partner to assist them with external data and analytics would be extremely/very important to their planning needs.  Read the full 2019 Executive Survey in Detail >>

2019-08-07T23:32:50+00:00August 6th, 2019|