Leverage All Available Data to Predict Customer Behavior
The customer experience that brands provide is becoming the decisive factor whether their clients will stay - or stray. According to Gartner, by 2020 more than 40% of all data analytics projects will relate to an aspect of customer experience.
To uncover the hidden gems that could turn them into the crown jewel of their industry, companies need to develop familiarity with their customers that is both deep and flexible. In this article we present you with our approach to fusing multiple data sources to measure each touchpoint with your customers in order to understand and predict their behaviour.
Holistic view of opportunities and “points of break”
According to Ivaylo Yorgov, Research Director at GemSeek, to optimize customer experience companies should pay close attention to the points of break where prospective and existing clients drop from the customer journey. As this could happen at virtually any stage, GemSeek’s holistic approach combines studies of brand image and brand equity with customer acquisition and customer experience analytics.
This comprehensive formula helps companies across industry to measure the pulse of their brand and identify opportunities for improvement along the customer journey. They can even gain competitive edge over their competitors by providing beyond what is expected.
Digging deeper into the moments of truth helps generate granular insights and design data-based solutions. Combining them with a ROI analysis of their potential impact gives companies powerful tools to prioritize actions.
Our philosophy: leverage all available data for business growth
Achieving business impact with data requires shifting from doing analytics for analytics’ sake to employing analytics as a way to address specific business challenges.
Whereas a recent McKinsey report disclosed that most companies capture only a fraction of the potential value of data and analytics, we propose an approach to not only unlock the value of internal databases, but also enrich it by collecting relevant information from research and third-party data sources. Indeed, taking advantage of the full-range of data collection methods allows us to conduct advanced analyses even when a partner does not possess great quantities of internal data.
According to Yorgov, bringing research, unsolicited feedback (social media comments, reviews, call records, etc.), and third-party data together is the foremost way to paint a full picture of the customer journey.
Similarly to the ancient art of alchemy which turned rocks to gold, the fusion of multiple data sources turns research reports into insight reports with greater granularity. Moreover, it allows companies to achieve an unrivaled level of familiarity with their client base, and to predict their attitude and behaviour with higher accuracy without being mind-readers.
The power of prediction
The unlocking of all customer data allows predictions of outcomes at every stage of the customer journey – from brand diagnostics to long-term customer retention.
Consider the following example from our portfolio to understand the role of multiple data source analytics in relation to brand optimization. A global leader in B2B and B2C electronics hired us to investigate whether the event for Radiology professionals they had organized positively impacted their brand image. The company wanted to discover what content most resonated with its target group, and contributed to positive brand performance.
Our digital analytics approach utilized multiple data sources to evaluate communications effectiveness, benchmark against global competitors, and optimize online presence. Building on data from social media, online journals and forums, and other relevant resources, we successfully discovered the “formula” to ensure our client’s brand harnesses the benefits of event marketing. To that end, we pinpointed the most effective channels of communication and most popular types of content, and recommended further actions to create online buzz.
When it comes to retention, our approach to utilizing data for predictions has been similarly robust. For instance, a major Swiss telecom we partnered with was struggling with retaining existing customers. They had started a churn reduction campaign, but low low response rates to surveys prevented effective targeting of churners.
To solve their challenge, we first analysed the behaviour and attitude patterns of identified churners. Using a combination of behaviour, experience, and demographics data, we built a machine learning model to predict which customers are at risk, and take appropriate actions to “rescue” them. In addition to dramatically reducing churn, our intervention served as a stepping stone for many company-wide initiatives to improve customer satisfaction and loyalty.
The ability to turn complexity into leverage requires bringing together all possible sources of customer data, and using them to reliably predict customer behavior. Our experience working with large multinationals on evaluating their holistic customer journey has yielded outstanding business returns. As customer centricity has become a company-wide goal for many of our partners following our analytical interventions, they have been able to significantly reduce overlaps, and thus affordably and consistently drive change.