Retail companies approach technology investments from different perspectives based on their evolution, growth trajectory, and operational maturity. The Mid-Tier is challenged to keep pace with larger and more R&D intensive players in Omni, Mobile, Personalization, Transformation, AI and Data Science with far fewer resources and budget. With the incredible opportunities emerging in retail, what are some of the tactical measures that can be employed to compete?
While IT has been whipsawed by strategic projects and pilots driven by transformation, the first question to ask is, have you taken the time to think about your “big block” core business applications and is your team keeping up with support? It is a questionable exercise to try and build advanced technology on or around an unstable Core. Given that numerous Mid Tiers don’t have the luxury of teams dedicated to projects, assess whether your team is spread too thin. What is the feedback you are receiving from your business partners? Are you deficient in deploying upgrades and patches? Do your key system integrations support the rapidity of data flow required? Is your change request backlog increasing/aging? Are you having issues covering peak demand periods?
Any gaps in these areas need to be addressed. Support for the Core is considered table-stakes and cannot be prioritized away. Adding low cost support, including offshore, in the form of temp and consulting resources, will help ensure timely and effective support while enabling the ability to execute against the strategic initiatives.
Data is perhaps the core asset of most enterprises. A myriad of challenges has caused friction within technology groups with respect to data management. In many organizations multiple technologies for sourcing, integrating, and managing the flow and consumption of data have materialized to support specific functional groups, reporting needs and prioritized projects. These issues have been exacerbated with the adoption of multiple analytical and visual reporting tools and dashboards.
With the growing adoption of advanced analytics, data management takes on greater importance. Developing an approach to your data management is a critical element and one where additional investment is warranted. Mapping data across current data stores, EDW’s, data marts and operational databases while developing a transitional approach to a flexible Cloud based big data and data lake architecture should be prioritized to support the analytic initiatives your organization is sure to be adopting.
Until you have a consistent approach to data management and integration, retailers will continue to struggle with managing, consolidating, and consuming data for their analytics objectives, not to mention incorporating non-standard and non-structured sources such as IOT devices, social channels and sentiment data, along with other third-party sources.
At this point there really doesn’t seem to be a question as to whether robust analytics will be on retailers’ roadmaps. Most firms have been focusing on operational and web based descriptive analytics and on financial operational metrics using visual reporting tools and dashboards. The proliferation of these toolsets has led to some of the decentralized data challenges firms now face in enabling more advanced analytic methodologies. So where should you focus and invest limited budgets?
The short answer is to go where the money is: customers, merchandising and inventory. The industry has quickly advanced to using predictive analytics to support customer actionable insights and personalization. Personalization throughout the customer omni journey will continue to proliferate as retailers focus on developing deeper relationships with their customer base. Having a strong CRM environment incorporating all consumer touchpoints and sentiment data will be crucial. In addition, embedding predictive analytic technologies in the Merchandising decision flow will increase the accuracy of modeling and forecasts and has the potential to enhance margins and optimize inventory levels.
Leveraging a consistent data and integration framework is going to be the key to supporting an analytics program at scale. To accomplish this goal, select a technology platform and stay within, as much as possible, its ecosystem of tools and solutions. There is a wide range of analytics tools and vendors and it will continue to grow. Maintaining solution-set consistency across organizational departments will enhance usage, adoption and the ability to support your analytics program as its complexity increases.
Change management is a critical component to an analytics program and most firms need some external support to transform the way work gets done and to make those improvements last. When selecting solution providers, identify those that understand both the technical and process implications of the desired changes and can work with both your technical and line of business organizations. Focusing on these areas should enable retailers to take a renewed assessment of their Core and ensure a solid foundation on which to continue to buildout advanced capabilities.