Is it really possible to predict customer behaviour?

Some companies and brands seem to be able to identify the zeitgeist better than others and leverage that to create products and services to which their customers build a loyal attachment. These visionary brands – such as Apple, Amazon, Facebook, or Google – create categories and markets seemingly out of thin air and establish dominant, almost unassailable, leadership positions.

Why do some brands have this magical ability to know what will happen or what will appeal to customers long before others do? And can any company build this capability?

Trends and lifecycles

It’s helpful to think first about your own experience as a consumer. Most of us are not early adopters of innovative new products. Instead, we wait and see who the winners are, then jump on the bandwagon once it’s rolling.

Let’s look at the product lifecycle for innovative new technologies (but it applies to most products and services) as defined by the famous Gartner Hype Cycle:

We see three interesting things in this diagram. An initial wave of hype accompanies most innovations – but who creates and drives this hype? The original innovator provides the spark – think Steve Jobs launching the first iPhone – and then the ever-hungry media and venture capitalists speculating on the next big thing driving the hype.

As customer expectations peak during the early part of the lifecycle, many copycat suppliers jump on the bandwagon, and most fail. What’s the difference between them and the original innovators who (usually) go on to dominate the new market once it establishes itself? It must be due to something other than the credibility that comes from being the originator of an idea. Facebook wasn’t the first social network but it was the most successful. The winners appear to be those with a strong and clear vision, which aligns with customers’ desires, and who have the operational chops to execute that vision.

Finally, most of us – as consumers – get on board only when an innovation is well up the slope of enlightenment towards the very right of the diagram. What drives us to do that then, as opposed to much earlier, or not at all? The answer is that we have seen proof that the product works by then. As with smartphones and social media, once adoption reaches a certain level the utility of the new product increases to the point where the pressure to join the crowd is impossible to resist – further increasing the innovation’s utility and driving more adoption.

This picture shows that companies and brands with a strong vision can create trends, and those with the ability to read trends can capitalise on them better than others. The inescapable conclusion is that businesses need to equip themselves with the capabilities to understand and predict the behaviour of their customers, as well as the broader market.

Drivers of customer behaviour

The world is a complicated machine. Few trends or businesses are driven by any one factor but rather by many influences that interplay in complex ways. As a rule, they include:

• Economic trends: Fluctuations in income, inflation, and job security directly impact spending habits and purchase decisions.

• Cultural shifts: Evolving values, environmental concerns, and conscious consumerism influence what we buy and why.

• Technological advances: Technology constantly reshapes how we discover and purchase products, from online shopping to social media recommendations.

• Product innovation: New offerings and features constantly emerge, influencing customer preferences and drawing their attention.

• Market dynamics: The rise and fall of brands, trends, and subscription models shift the competitive landscape.

• Pricing strategies: Well-targeted pricing can attract customers while overpricing can deter them.

• Marketing campaigns: Effective campaigns build brand awareness and influence purchasing decisions.

• Customer service experiences: Positive experiences foster loyalty, while negative interactions can lead to dissatisfaction and churn.

• Quality and value: A product’s inherent functionality, durability, and perceived worth drive customer satisfaction and repeat purchases.

• Product lifecycle: New products attract attention, while outdated ones lose appeal – and innovative products follow the hype cycle.

• Brand reputation and perception: A positive brand image builds trust and loyalty, while a negative one can lead to customer defection.

Given all those touchpoints and potential influences working on customers, is it possible for any company to build the capability to predict how its customers, or entire markets, will act in the future?

The power of predictive analytics

Predictive analytics uses statistical algorithms and machine learning to anticipate customer needs and behaviours, potentially down to the level of the individual customer.

Historical customer data and interaction data tell you what customers have done in the past, but with the power of AI, you can now leverage that same data to predict what will happen in the future.

While prediction is not prophecy, predictive CX analytics can give businesses a deeper insight into the complexities of customer behaviour and enable them to predict:

• Purchase potential: Predict what products or services a customer will most likely be interested in, enabling personalised recommendations and targeted marketing campaigns.

• Forecast demand: Utilise predictive models to anticipate future sales trends and optimise inventory management. This ensures efficient operations, avoids stockouts, and minimises unnecessary storage costs, contributing to overall business agility and customer satisfaction.

• Upsell and cross-sell opportunities: Anticipate customers’ future needs and recommend relevant products or services before they even ask, creating seamless and delightful experiences.

• Churn risk: Identify customers at risk of churning, allowing you to intervene with targeted loyalty programmes or proactive support.

• Customer lifetime value (CLV): Determine which customers hold the most potential for long-term revenue, focusing your efforts on nurturing their loyalty.

The strategic application of data science and machine learning offers businesses a wealth of benefits, including:

• Competitive edge: By anticipating customer needs and tailoring offerings accordingly, businesses can gain an edge over competitors and stand out from the crowd.

• Enhanced customer experiences: Tailor marketing, recommendations, and customer service to individual preferences and predicted behaviour. This creates a unique and relevant experience for each customer, fostering loyalty and engagement.

• Data-driven decision-making: Predictive insights can inform strategic decisions across all aspects of the business, helping you navigate the complexities of the constantly evolving marketplace.

• Proactive retention: Identify customers at risk of churn and implement targeted retention strategies. Offer personalised incentives, address concerns proactively, and demonstrate that you value their business. Building stronger relationships prevents customers from slipping away.

• Dynamic pricing and promotions: Refine pricing and promotional strategies based on customer segments and value perceptions. Use data insights to offer personalised deals and pricing that resonates with individual needs, helping you to maximise both profit and customer satisfaction.

Implementing predictive analytics

Predictive analytics uses software like SAS, R, Python, data mining tools, and machine learning platforms to analyse data from multiple sources. The process includes the following steps:

1. Data acquisition and preparation: Gather relevant data from multiple sources (e.g., purchases, website interactions, surveys). Ensure it’s clean, complete, and formatted correctly for analysis.

2. Selecting the right tools: Different algorithms have different strengths. Choose the one best suited for your goals, whether predicting purchase amounts (e.g., linear regression) or identifying customer segments (e.g., decision trees).

3. Model training and learning: Feed the prepared data to the chosen algorithm. This allows it to learn patterns and relationships within the data.

4. Model evaluation and refinement: Test the model’s performance on previously unseen data. Analyse its accuracy and applicability, making adjustments as needed to improve its effectiveness.

5. Integration and monitoring: Once satisfied, seamlessly integrate the model into your business processes. Continuously monitor its performance and make adjustments as necessary, ensuring it remains accurate and delivers valuable insights.

Depending on precisely what information your programme is trying to uncover, you will analyse your data using the following types of models:

Clustering models: Group customers based on shared characteristics, creating distinct “islands” with unique needs and preferences. This allows for personalised marketing and tailored experiences for each segment.

Propensity models: Predict the likelihood of future customer behaviour. By identifying what customers are like to do based on their past behaviour, brands can take proactive measures to prevent churn or increase the value of high-potential segments.

Collaborative filtering models: Recommend products or services based on the preferences of similar customers. This “you might also like” feature is a powerful tool for driving engagement and increasing sales.

Forecasting Models: Predict future demand, traffic, and staffing needs based on historical data and customer trends. This ensures efficient resource allocation and a more efficient operation, leading to a better customer experience.

Optimisation Models: Analyse trade-offs between various CX elements, such as pricing, contact policies, and service options. These models help identify the optimal strategies to maximise customer satisfaction and business outcomes.

Churn Models: Identify customers at high risk of leaving, allowing brands to address their concerns and proactively prevent churn. Retaining existing customers is often more cost-effective than acquiring new ones, making this a crucial model for long-term success.

The benefits and limits of predicting customer behaviour

Advances in artificial intelligence, natural language processing, and even neuroscience offer exciting possibilities for understanding customer behaviour. These developments promise deeper and more nuanced insights into the human mind. However, it’s vital to remember that technology is a tool, not a magic solution.

While predictive tools can inform business decisions and enhance personalisation, they shouldn’t be mistaken for guarantees or replace human judgment. Building strong customer relationships demands trust, transparency, and a commitment to ethical practices. Harnessing the power of prediction responsibly, with a human touch, is key to navigating the complexities of customer behaviour.