Building a Clear View of Your Customers
The infographic highlights the key challenges faced by businesses as they create a customer view through unified data, analytics, and more.Download the Infographic
Big Data in the retail industry is having its moment in the spotlight. Thanks to digitization, the growth of IoT, the continued rise of social engagement sites and technological advancement, data is easier to capture than ever.
Retail analytics alone has collected such an unprecedented amount of data that it’s been hailed as the “new data industry” by CIO magazine. When processed and analyzed, you can use this information to promote personalized customer engagement, increase employee productivity, identify new business opportunities, and improve operational efficiency.
However, despite the vast wealth of customer data available to businesses and the clear benefits of retail analytics, the industry has only captured 30 to 40 percent of its projected value due to barriers such as a lack of analytical talent and siloed data within companies. Far too often, businesses will invest in a retail data analytics solution without any clear idea of what their data strategy is or how they intend to act on the insight that solution provides, making it harder to understand how to utilize that data.
Don’t miss out on the incredible potential of Big Data retail analytics — take a look at these common, costly mistakes retailers make with data analytics, and learn how to avoid making them yourself.
Mistake #1: Investing in a tech-only solution.
Much has been made of how emerging technology is driving progress in the retail industry. Too many retailers wrongly assume that data analytics technology alone will solve all of their problems and lose sight of their people and processes as a result. While you should certainly consider investing in retail data analytics technology to better optimize business operations and maintain relevance, you should avoid going all-in on tech at the expense of the human element.
Solution: If your employees don’t understand how to use the technology, how to interpret the data it provides, or how to put that data-driven insight into action, you’re likely to encounter the same issues you’re trying to avoid. Invest the time and resources to ensure that your employees are well-versed in the technology. It will pay dividends, and employees will feel empowered to do their job properly.
Mistake #2: Thinking a “one-size-fits-all” solution is the answer.
In an ideal world, you’d be able to use a single retail analytics solution across your entire organization. In reality, however, this “one-size-fits-all” approach is highly impractical. Simply put, retail businesses aren’t homogeneous; they consist of hundreds, even thousands, of business users, employees and other people, all of whom think and behave differently. It’s in your best interest to look for a toolset with the flexibility to accommodate this diversity of thought and action.
Solution: First, you’ll need a visualization service that makes data easily digestible by end users, business analysts and IT personnel alike. Next, implement visualization tools and services that enable you to create detailed, highly formattable reports that provide a clear view into every level of your business’s operations and every stage of the sales process. Finally, look into a toolset that operates on a self-service model that’s intuitive and easy-to-use for members of all levels of your organization.
Mistake #3: Insufficient investment in workforce and workforce development.
According to Gary King, Weatherhead University Professor at Harvard University, it isn’t the sheer volume of data available to businesses that’s revolutionary, but rather that businesses are able to do something with that data. However, that means very little if you can’t interpret that data or you lack employees that know what to do with it.
You’ve probably heard from a number of outlets about the skills shortage in the data science and analytics field. As recently as 2015, 40 percent of companies reported that they struggle to find and retain data analytics talent, according to a survey from MIT; that percentage has likely increased with the growing demand for data management and interpretation skills.
Solution: One tactic that companies might consider is turning to their local colleges and universities. These quasi-talent pools are full of capable, in-training analysts that will be looking for employment after graduation. Consider speaking with the faculty and deans of the business and analytics departments about possible partnerships or internships.
Additionally, many colleges have their senior students complete capstone or thesis projects which involve working with businesses on actual projects. You may consider participating in those programs. You will be able to get a first-hand look at the talent, learn about new technologies, as well as get a business challenge addressed for little to no cost other than time invested with the students.
Mistake #4: Lack of defined metrics.
Even if you are able to find a qualified retail data analyst, there are still the matters of defining key metrics and training employees. It’s important to define key metrics because it gives employees a set of achievable, quantifiable goals to work towards and prevents your workforce from being spread too thin.
Solution: Make sure your analysts have the requisite knowledge and tools to use for Big Data and retail analytics. Be sure to invest in high quality, comprehensive employee training, specifically training that covers the role of data analytics in the retail industry. Also, when shopping for a retail data analytics solution, consider purchasing one that keeps employee engagement and productivity in mind and empowers them with the tools they need to do their jobs well.
Mistake #5: Creating an analytics culture from the bottom-up rather than the top-down.
When it comes to developing their company’s retail data analytics culture, many prefer to take a bottom-up approach because it uses data as a jumping off point and creates a more accurate market forecast on which to base their business strategy. There’s just one glaring issue with this approach: It forces you to tailor your business strategy to fit the data, which could run contrary to your company’s needs.
Solution: Take a business-outcome approach to the technology. Start by identifying the problem you are trying to solve, conduct a proof of value on that objective, confirm the results and make the investments in the technology.
Using a top-down method for your retail data analytics culture is the way to go. It prioritizes business initiatives, uses Big Data to optimize operations, and forces team to work collaboratively to achieve a broader goal while breaking down data siloes in the process.
This approach also saves time, money, and effort because it doesn’t require the purchase of extra tools and systems and enables employees to focus their efforts on achieving the goals you set. With the right solution, top-down retail analytics can even provide a broad view of the market, enabling analysts to identify patterns and make data-driven decisions to propel business growth.
Mistake #6: Creating data and analytics silos.
Silos are one of the biggest obstacles to taking full advantage of data analytics in the retail industry. Silos arise organically out of divisions between organizational departments; different departments have different objectives and are sometimes hesitant to share their data and resources, often at the expense of identifying valuable business opportunities.
If you’re experiencing issues with data silos, you’re in good company: Although 73 percent of companies consider themselves analytically-driven, only 38 percent share insights outside of their department, according to a report from Dun & Broadstreet. And once they become entrenched in company culture, silos are almost impossible to eradicate—that said, there are still measures you can take as a retailer to mitigate the damage done by silos.
Solution: Start by creating a consistent interdepartmental methodology for handling Big Data. Rather than separate members of your retail data analytics team into separate silos based on who collects, processes, and makes decisions with data, turn it into a collaborative process. Most importantly, make sure to use an operational solution that offers seamless integration not only across departments, but also across systems to promote data and resource sharing, collaboration, and transparency.
The foundation of any foolproof retail analytics strategy is an analytics-enabled business solution—and not just any solution, but one that offers ongoing support, vertical integration, and quality insight into how to keep customers engaged and how to streamline back-end operations. Contact the team at Hitachi Solutions today to find out how our comprehensive, integrated software suite can help you use data analytics to your advantage.