In the retail sector, the allure of data monetization is compelling. The promise of leveraging insights to not only enhance customer experience but also create new revenue streams is a persuasive one. However, the path to monetizing retail data is not linear.
The stakes are high: Gartner predicted that by 2025, 80% of organizations seeking to scale digital business will fail because they do not take a modern approach to data governance. The journey is filled with nuanced challenges that, if not addressed, can impede data transformation into dollars.
As retail leaders, recognizing these anti-patterns is essential to avoid the pitfalls that can derail retail data strategies.
Anti-pattern 1: The Quicksand of a Fragile Data Foundation
One of the most common pitfalls in retail data monetization is the reliance on a data foundation that is not robust, agile, and flexible to adapt to a fast-changing business environment. A robust and scalable data architecture is non-negotiable. Retailers often struggle to integrate various types of data swiftly and cost-effectively due to outdated or rigid systems.
While most business leaders believe that data is an integral part of their business strategy, only a handful trust their data to make important business decisions. The message is clear: investing in a modern, flexible data infrastructure that easily assimilates diverse datasets from first and third-party sources is crucial. This infrastructure must not only handle volume but also variety and velocity, ensuring that retailers can capitalize on data without excessive overheads or delays.
Anti-pattern 2: The Peril of Executive Indifference
Retail data monetization initiatives often falter due to a lack of executive buy-in. Leaders must understand that establishing external data partnerships and modernizing data systems requires time and investment.
A study by NewVantage Partners revealed that 92% of companies are increasing their pace of investment in big data and AI, yet only 48.4% are managing to become data driven. The takeaway is that patience is needed, and executives need to seriously assess priorities for investments in Data & AI. The initial stages may not yield immediate results, but the long-term value is substantial. Leaders must commit, both ideologically and financially, to see these projects through to maturity.
Anti-pattern 3: Prioritizing Data Quantity Over Quality
Retail data monetization is significantly hampered by poor data quality. When customer data is inconsistent across digital and physical retail channels, it can lead to misaligned marketing strategies and an incomplete understanding of customer behavior. For example, a retailer might witness a decline in campaign effectiveness due to disparate customer profiles. By consolidating and cleansing their data, such a retailer could not only improve the targeting of their promotions but also see a notable uptick in sales efficiency.
Similarly, a clear and unified attribution model is essential for retailers to discern which marketing efforts drive sales, allowing for optimized allocation of marketing spending and improved ROI across different channels. Prioritizing data integrity and addressing data anomalies thus become a strategic imperative in realizing the true value of data monetization initiatives.
Anti-pattern 4: The Fear of Investment in Retail Media Networks
Many retailers hesitate to invest in building their own media networks due to the perceived risks and substantial investments involved. However, this overlooks the potential for executing cooperative campaigns with CPG brands. By positioning brands strategically within their digital ecosystems, retailers can unlock additional revenue.
By co-creating targeted campaigns within their website and app, both parties can share the investment and benefits. For instance, Walmart's retail media network (Walmart Connect) has successfully harnessed its vast trove of customer data to deliver targeted retail advertising, substantially boosting its non-trading income.
Anti-pattern 5: Under-estimation of ROI from Personalized Experiences
The skepticism around the ROI of personalized customer experiences in retail is a common anti-pattern. However, it dismissively ignores the power of personalization at scale. Businesses that embrace this strategic edge—employing advanced analytics and AI in retail to customize interactions across every touchpoint—tend to register not just a spike in sales but also bolstered customer loyalty.
In an age where consumer preferences rapidly evolve, those retailers who adeptly employ personalization stand to gain a competitive advantage, securing not just a transaction but a lifelong customer relationship. This investment in personalization technology thus transcends short-term gains, paving the way for sustained revenue growth and brand differentiation in a crowded market.
Anti-pattern 6: Security Gaps and Brand Integrity Risks
The concern surrounding data security in retail data monetization cannot be overstated, as even a single breach can tarnish a retailer's reputation and erode customer trust. Secure data-sharing infrastructures like data marketplaces and cleanrooms are not just protective measures—they're strategic assets. These platforms enable safe data exchange with partners like CPG firms and advertisers and affirm a retailer's commitment to data stewardship.
By adopting these secure environments, retailers uphold stringent data privacy standards, reinforcing their credibility in the market and safeguarding their customer relationships. As data privacy regulations become increasingly rigorous, investing in such secure solutions is not a discretionary choice but a business imperative that underpins sustainable data monetization strategies.
Rising Beyond the Pits
While retail data monetization offers immense opportunities, it has its challenges. Retailers must build a solid and scalable data foundation, secure executive buy-in, insist on high-quality data, overcome the fear of investment in retail media networks, recognize the ROI from personalized experiences, and ensure secure data-sharing practices.
These are not mere operational tweaks but strategic priorities that demand attention at the highest levels to ensure maximized business value. Retailers that successfully navigate these pitfalls will be well-positioned to unlock the true potential of their data and lead the pack in the competitive retail landscape through insights-driven decisions.
AUTHOR - FOLLOW
Changa Reddy
Vice President, Tredence
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