Strategising Customisation and Privacy in the Digital Age

by David Dubois, INSEAD Associate Professor of Marketing, and Joanna Teoh, INSEAD

Five golden rules to effectively balance personalisation and customer protection.

From AI-enabled chatbots to ads based on individuals’ search or social media activities, digital data offer novel ways to connect with customers. These connections can develop into intimate customer relationships that boost satisfaction, engagement and ultimately loyalty. Consider Netflix’s recent personalisation strategy, which enabled viewers of its series Bandersnatch to choose the main character’s actions throughout the episode, leading to five unique endings.

But there is a point where customer intimacy and invasion of privacy blurs. For instance, as early as 2012, Target predicted a teenage customer’s pregnancy through her historical purchase pattern data and sent her baby-related coupons, to the surprise of her own parents.

Where to draw the line between customer-benefitting personalisation and intrusion? This question is increasingly at the heart of every C-level executive’s agenda. In the digital age where data has become overabundant – 90 percent of the world’s data was produced in the last two years – corporations face the urgent need for a “data chart” defining their philosophy around the collection and use of customer data as it relates to value creation.

Broadly speaking, the company policy should articulate two key principles. First, it should define the extent to which the data can help strengthen and enhance interactions along the customer journey. It should specify the kinds of touchpoints and their frequency (transactional dimension), as well as the type of connection and nature of interactions (relational dimension).

Second, the policy should clearly delineate what customer data to collect and how it is collected. In other words, it should define what customer privacy means and what it doesn’t mean. This directly relates to customer intimacy – the insight-driven deepening of the relationship through greater customisation.

In practice, here are five golden rules to keep in mind when designing your data policy. We have found them to be most helpful to business leaders as they articulate the company’s stance.

  1. Don’t suffer from “data FOMO” (fear of missing out)

It is tempting to get into a race to collect and use (ever more) data. This is in large part triggered by the fear that one may lose ground to more data-savvy competitors. In short, executives too suffer from the fear of missing out syndrome. They focus on what data they have relative to competitors, rather than what data may create value for their customers. It’s worth remembering that how one acts on data – rather than how much data one has – predicts future business success. In other words, successful competitive advantage rests on leveraging and integrating “the right data” into decision making to enrich customer value.

The source and nature of insights integration is highly industry- and even firm-specific. For example, with 93 percent of client engagement taking place on Instagram, luxury brands have no choice but to use the photo-sharing app to monitor brand sentiment and reputation. This is also where they can integrate the resulting customer-driven insights into their communication strategy. In the pharmaceutical industry, companies increasingly focus on sensors and devices to generate valuable real-world evidence and facilitate R&D growth.

  1. Ask for customer consent and embrace local regulations

Knowing how to best protect customers (even those willing to share a lot of data with you) is paramount. Make sure to adhere to the laws that govern data collection practices in the target market. The CNIL (Commission nationale de l’informatique et des libertés) offers a great interactive map that gauges the world’s data protection policies against the EU’s and can help you assess when and where personal data may be transferred.

Customer consent must be simple, fast and non-intrusive. Progressively requesting access to data may be more effective and less intrusive than requesting consent for all data points at an early stage. Keep in mind the give-to-get ratio to effectively build trust over time. Moreover, integrate opt-out features liberally, such as in the privacy policy as well as alongside the ads themselves.

  1. Focus on situational and relational (rather than only transactional) customer data

Knowing your customer remains the cornerstone of effective marketing strategy. Combined with traditional methods (e.g. on-site observations or customer surveys), digital analytics can identify customers’ unmet needs and aspirations with greater accuracy and reveal how the product fits into their lives. In particular, digital analytics help to mitigate conscious or unconscious biases associated with traditional market insights techniques. It does that by uncovering actual online behaviour, for instance what people search, like, the kinds of websites or actions they perform while using an app, etc.

Using a situational approach, the data chart should focus on consumer contexts and moments in the journey where collecting and acting on the data provides significant value for the customer, in alignment with the business’ positioning. Whenever requesting data through surveys, think about the format, framing and flow of questions. Consider potential sources of sampling biases (e.g. how the question is framed, the sample size). Be transparent and justify requests for data. This will help to build up trust over content collection and generation.

  1. Transform data into actions through continuous testing and learning

Implementing small, targeted tests is the best way to ensure that data are clean, relevant, useful and impactful. A recent study by Experian suggests that on average, 30 percent of data being used for marketing is inaccurate. To illustrate, Gillette recently sent a 50-year-old woman a birthday kit including a new razor, welcome-to-manhood messaging and related marketing materials.

To avoid these pitfalls, testing data quality and relevance through micro-actions (e.g. small tests on small segments or markets) is essential. Customer journey mapping centered on specific pain points and meaningful moments can be a powerful tool to launch small actions and gauge customer reactions before increasing personalisation. Over time, you can learn what type of data will matter most and also when customers may respond more vs. less positively to customisation. In a recent example, Benadryl used a combination of pollen count and social media sentiment to offer targeted ads and localised information for potential customers.

Being proactive with data requires setting up new organisational processes that support data usage and collection as part of your digital transformation. These include adopting data protection capabilities to accompany data collection. It also involves setting up regular cross-functional meetings to discuss how to act on the data, as well as to review and communicate the results of small-scale experiments. This will enable the company to learn and adapt its data practices continuously.

  1. Develop data-driven outstanding customer experiences

With great storytelling, data and personalisation can come alive in ways that engage and inspire customers. One way to accomplish this is with “data-based creativity”, such as The Next Rembrandt by ING Bank, which used deep learning and face recognition to create new artworks based on “the artistic DNA” of the master. “Creative use of data is another unique approach – Nike built a LED running track in Manila, Philippines that technologically generates an image of yourself to compete against.

In other contexts, timely assessment of customer feedback can help businesses improve satisfaction and service recovery. For instance, AccorHotels pioneered a data-driven insights strategy at the local level by empowering hotel staff to react and respond to negative reviews or social media comments captured “live”.

Effective customisation in a digital age: Where to start?

In sum, it is important to put customer value at the centre of one’s efforts to design a data-driven strategy. To what extent will customers be better off once the strategy is implemented? The answer to this question often requires recontextualising your data strategy. That is, to identify key moments in the customer journey where additional insights can be leveraged for the customer’s benefit. Increasingly, customisation will require mixing offline and online data.

The omnichannel approach sheds light on the end-to-end customer journey, establishing a delicate balance between respecting customer privacy and data-driven value creation. Achieving this will ultimately maximise consumers’ psychological, social and financial welfare.

Source: INSEAD

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