Data Science: Connecting with the Humans Behind the Data

Marketers in the digital age are awash in data. Terabytes of data pour in daily from web analytics, purchase history, and social media, opening unprecedented opportunities to understand customer behaviour. New data science capabilities help us discover hidden insights to optimize decisions across the marketing mix. 

Yet, in our enthusiasm for crunching customer data, we risk losing sight of the living, breathing humans behind the numbers. Data science augments but cannot replace an empathetic understanding of customer needs—the most successful brands balance data-driven precision with human-centred wisdom.

This article explores practical strategies for marketers to harness data science while connecting with customers on a profoundly human level.

Actionable Segmentation

Grouping customers into segments is a cornerstone of strategic marketing. Data science enhances traditional segmentation approaches through the following:

1. Behavioural clustering: Machine learning algorithms can analyse purchase history, web activity, and social data to uncover unexpected customer similarities, allowing for more strategic targeting.
2. Propensity modelling: Predict which customers are most likely to purchase certain products, respond to specific messages, or disengage. Address each segment accordingly.
3. Multivariate testing: Test messaging variations tailored to different segments simultaneously, then double down on what resonates best.

The key is to balance data-driven segmentation with qualitative customer insight. Embed ethnography, surveys and open-ended interviews into the process. Don’t just look at who responds, but ask why.

Predicting the Future 

Forecasting customer behaviour enables proactive marketing strategies. Data science predictive analytics tools allow us to:

1. Calculate customer lifetime value and prioritise high-value customer retention activities, potentially through trigger-based communications.
2. Identify customers showing pre-churn signals and deploy specific retention campaigns, including usage-based engagement prompts.
3. Determine propensity to purchase certain products and services, then align personalised recommendations and offers.

While forecasts help inform strategy, the future remains uncertain. Predictive analytics should trigger further direct customer dialogue rather than fully automated messaging. Seek opportunities for open-ended feedback through post-purchase surveys, community forums, etc., to uncover unseen needs.

Optimising Experiences

Data also plays a key role in optimising customer touchpoints by:

1. Leveraging multivariate testing to continuously refine content, offers, product pages, etc., based on engagement and conversion data.
2. Employ geo-targeted dynamic content to tailor information to a user’s location.
3. Building intelligent recommendation engines that suggest relevant products using collaborative filtering algorithms.
4. Implement chatbots that offer personalised support based on past interactions and purchase history.

While data drives customisation at scale, avoid overtly robotic experiences. Chatbots and web personalisation systems should demonstrate emotional intelligence, humour and natural language capabilities. Ethnographic user research helps uncover unmet needs and desired feelings that data science cannot detect. Qualitative insight combined with quantitative rigour creates truly empathetic engagements.

Measurement and Experimentation

A key advantage of data-driven marketing is the ability to run controlled experiments and precisely measure outcomes. Strategies include: 

1. Multivariate testing of campaign elements like images, headlines and calls-to-action to determine optimal configurations.
2. Geo-targeted regional pilot programs before total investment in nationwide rollouts.
3. Algorithmically generated customer treatment groups and holdout sets for controlled measurement.
4. Implementation of changepoint detection algorithms on time series data, revealing shifts in trends to inform strategy pivots.

Yet, measurement changes what is being measured. Customers may feel differently about overtly experimental messaging over long periods. Honour people over percentages through radical transparency about testing and responsible data practices that respect privacy. Provide opt-out mechanisms from continuous experimentation while keeping channels open for qualitative feedback.

Overcoming Biases and Blind spots

For all its power, data science inherits problems from the humans behind it—algorithmic, demographic and cognitive biases skew outputs.

Strategies to counteract biases include:

1. Performing bias audits on data and algorithms through statistical analysis of subgroup performance.
2. Leveraging techniques like resampled bootstrap aggregation (“bagging”) to reduce overfitting on limited datasets.
3. Seeking diverse perspectives from team members and customers - on proposed models and segmentation strategies.

Proactively questioning assumptions, continuously enhancing input data diversity and preserving flexibility will help data science marketing strategies resonate across the full spectrum of humanity.

The Heart of the Matter

Data science allows marketers to engage customers with relevant and personalised experiences. Yet no algorithm rivals the human capacity for imagination, emotion and relationships. The most successful brands balance data-driven personalisation with courageous thinking and genuine empathy.

While data provides an invaluable compass, the human spirit remains the wind in our sails. Data science helps predict customer behaviour - human science explores how to elevate it. By interweaving both into our strategies, we can create marketing rooted in statistical rigour yet realised with profound humanity.