Jackie Shoback is cofounder and managing director of 1414 Ventures, an early-stage venture capital fund focused on digital identity.
In the current data-driven economy, fostering growth, profitability and brand equity necessitates leveraging precise data insights derived from customer behavior, market dynamics and business transactions.
The more data you’re able to capture, the more you can glean insights and opportunities—everything from pricing to service improvements to new product innovations and untapped markets. According to MIT’s Technology Review, transforming data into a capital asset can be the difference between an organization that stagnates and one that thrives.
AI tools and platforms that accelerate the growth of data promise a more efficient way to build brand equity and customer loyalty; however, with the speed and ease of building data capital comes a need to balance and ensure that CEOs and executive teams don’t overstep their bounds and break trust with their customers.
Specifically, I find that respecting privacy, trust, transparency and equality in the usage and activation of a company’s data capital is key. Therefore, I believe it’s important to find the optimal balance between leveraging your data assets and enhancing brand equity as you integrate new AI tools.
Building Brand Equity And Business Value
Brand equity, according to one definition by Prophet, is a marketing term that describes a brand’s value. That value is determined by consumer perception of and experiences with the brand. If people think highly of your brand, it has positive brand equity.
Brand equity develops and grows as a result of a customer’s experiences with the brand. The aggregation of all those experiences helps shape the brand perception or equity, which in turn drives customer loyalty and customer lifetime value. The better the customer experience, the higher your brand loyalty and, thus, the better your customer economics. Ultimately, this impacts growth, profitability and market share.
In my experience, improvements in customer experiences, operational efficiency and reduced friction are all possible outcomes when you effectively analyze your customers’ behaviors and transactions. But while ingesting data into generative AI models with machine learning (ML) can help you predict customer needs and more efficiently serve them, the potential confidentiality of the data can put those relationships in jeopardy.
Therefore, your marketing, operations and IT all need to agree upon a data capital strategy that is both effective and safe. Ask yourself:
• What are our business objectives/outcomes?
• What data attributes will we use?
• How did we procure it?
• Do we have the appropriate consent?
• What is the data lineage?
• Can we ensure our data sets are protected, privacy is preserved and we have tools to detect data leakage?
All these factors need to be carefully weighed when implementing data capital strategies to avoid potential negative brand equity, lost revenue and diminished profits due to breached trust with your customers.
Data As Capital And A Value Driver
Put simply, data capital is the recorded information necessary to produce a good or service. And it can have long-term value just as physical assets like buildings and equipment do. Data can help create better services, improve operations and drive value throughout your organization. In business, there is a virtuous cycle that exists between data capital and brand equity.
The more trust and brand equity built, the more a business can gather about its customers from its interactions and behaviors—ultimately better meeting unmet needs and delivering more completely upon customer expectations and promises.
Four Tips To Balance The Velocity Of Data Capital
As businesses seek to operationalize data insights into their business models, new risks and rules need to be considered to ensure privacy preservation and security.
Here are a few tips to consider as you seek this balance.
1. Create a data governance steering group with cross-functional representation to ensure business strategy and data strategy are aligned.
2. Request legal, risk and compliance (if pertinent to industry). Opine on privacy laws/rules and generally accepted practices to ensure C-suite leaders know the privacy requirements and boundaries.
3. Assess and audit existing processes and workflows to identify any vulnerabilities or gaps vis-a-vis data privacy and security protocols.
4. Analyze output from new AI tools and LLMs for privacy leakage and reverse engineer in order to test whether confidential personally identifiable information (PII) is discoverable.
More precise and personalized insights can delight customers and exceed their expectations. AI tools and platforms promise to allow for quick and precise predictions of customer needs, but you need to make sure to collect this data with care and trust. Simply put, striking the right balance between consuming data capital and building brand equity helps to keep your customer’s best interest at the center of your business strategy and execution.
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