How to leverage retail analytics to deliver maximal business value

‘How can we improve the impact of our data initiatives?’ is an increasingly common question we hear from retailers. Though most of them already generate an abundance of data from a multitude of sources, many admit that they find it hard to leverage their data to deliver measurable business value.

The rapid acceleration of e-commerce, supply chain hyper-agility and the need to meet customers where they are all make data analytics critical to a retailer’s ability to survive and thrive in a highly competitive global marketplace. At the same time, availability of customer data is at an all-time high. The more digital transactions a company has, the broader its data-driven process can be. The fact that there are so many possibilities means that prioritization in alignment with business goals is absolutely essential. Depending on what a company’s business strategy and priorities are, it will make sense to use its data for different purposes.

With all of these trends in mind, many retailers have made significant investments in systems that increase their digital interactions, as well as capturing and analyzing as much data as possible. All too often, though, they report that their data initiatives to date have generated mixed results at best. Based on BearingPoint’s experience in this area, I believe there are three main reasons for this:

1. Lack of strategic direction
2. Too much focus on technology
3. The proof-of-concept (PoC) trap

#1 - Lack of strategic direction

Many retailers’ data and analytics initiatives lack a clear connection to the company’s strategic goals. This makes it hard to create business value and to align different initiatives in a common direction. Without strategic direction, it is also common to face challenges with implementation and business adoption.

The best way to overcome the problem of a lack of strategic direction is to define a company-wide data and analytics (D&A) strategy that is owned by the management team. To ensure that data projects generate real business results, it is essential to have a D&A strategy that is firmly rooted in the company’s overall business goals.

#2 - Too much focus on technology

Retailers with a technology-driven approach typically start off by investing heavily in data and analytics technology, “gathering all the data”, hiring data scientists and/or choosing a machine-learning algorithm. This might result in a fancy technical solution and many analyses, but it is an ineffective way to gain tangible business results. The battle isn’t about having the best technology – it’s about using it correctly.

While technology often plays a key role in transforming a business, it is important to remember that technology cannot change anything on its own. As exciting and full of promise as many new technological solutions are, they are only one component of the digital transformation of any organization. The only way for technology to achieve a measurable business impact is for the relevant people in the organization to embrace the new technology and use it as part of a larger effort that includes changing the appropriate business processes.

#3 - The proof-of-concept (PoC) trap

Passionate subgroups in retail organizations often develop a bottom-up approach to data by identifying and developing promising prototypes (a.k.a. PoC) that can then be rolled out in the business. This approach often tends to lack focus and lead to complex technical solutions. Lack of commitment and funding makes it hard to scale up promising initiatives to a data-driven way of working throughout the company.

The fundamental difficulty with this approach is that an IT-only data project team can generally only do about 30-50% of the work that a PoC requires. The rest of the work needs to be done by other business functions. If those other functions are not involved in the project early on, chances are high that the PoC will stall and ultimately fail. To be successful, PoCs need to be closely aligned with the key business goals and priorities of the retailer.

What is a data & analytics strategy?

A D&A strategy defines how to seize the opportunities that data provide while building on the company’s current situation and avoiding pitfalls along the way. The most successful D&A strategies are defined with wide and active engagement in the organization, which helps the organization establish a common language with respect to data and analytics topics. In essence, the D&A strategy should answer three questions:

• Why do we need D&A in our business – what is the strategic and financial motivation?
• What should we use D&A for – what are the use cases that are most important for us?
• How should we set up our D&A capabilities, including technology, organization, ways of working and governance?

We believe that defining the use cases as a central part of the strategy is key since it provides the link between the overall business goals and the required D&A capabilities, in a language that can be understood by both business and technology teams in the organization.

How BearingPoint can help

At BearingPoint, we combine business understanding with technical expertise to help clients become data-driven and reach their goals through insight-driven actions. Our data strategy framework is focused on defining value creating use cases based on the business goals and identifying what is required to enable those use cases. The goal is to clearly define the qualitative objectives and desired financial results of leveraging data. Together with our clients, we quickly identify high potential use cases with the help of a Value Creation Assessment.

What’s the situation at your company? Are your data initiatives delivering the results you expected? If you’d like to learn more about how BearingPoint can help you realize measurable business value through your data initiatives, don’t hesitate to contact me.