With radical changes abound, the business world is in flux. Digitalization, AI, Big Data and other new technological developments continuously disrupt traditional distribution channels and business models, and require rapid shifts under the threat of obliteration.
Tomorrow, more so than ever before, the best performing companies will be listening to their customers to stay relevant, and build successful products and services. The ways companies will leverage customer feedback to secure their leading position in the marketplace will be profoundly different than today’s.
In this article we present four technology-driven trends that we believe will play an increasingly important role within the research industry in 2019, to enable organizations to better utilize customer feedback, and predict customer behaviour.
Trend #1: Listening to conversations
When several years ago Joan Lewis, Global Insights Head of P&G, claimed that “survey research will decline dramatically in importance by 2020. Social media listening will replace much of it, adding new dimensions,” his prediction was met with disbelief. Yet, as Lewis anticipated, the surge of social media listening approaches has not only aided the market research industry, it has virtually transformed it.
In order to stay afloat and have a shot at industry leadership, organizations can no longer merely ask questions to gauge the success of their products and marketing initiatives with their customers. Instead they need to be equipped to listen in on the conversations that their own clients initiate, and have full control over.
Whereas the rise of social media and the power customers’ have extracted from it to voice and distribute their opinions is threatening to the underprepared, leading organizations have met this shift with open arms, and used it to enhance their customer centricity. Social media listening has become a standard in community management, and will continue to increase in importance throughout 2019.
Affordable and timely, social media listening helps companies better understand customers, and encourage loyalty and advocacy. Drawing on real-time feedback provided by customers, businesses can monitor their brands’ health, and distinguish themselves from competition. The deep analysis of data obtained through social media listening can help uncover hidden underlying issues, as well as opportunities, in addition to facilitating proactive action.
Trend #2: Quit surveying en-masse
Whereas large-scale surveying based on customer segmentation will not disappear altogether, it will reduce dramatically in 2019. With personalization a top priority for customers globally, personalized, in-the-moment surveys will become the norm in customer experience management.
Although more than 79 percent of U.S. consumers and 70 percent of UK consumers expect personalized experiences from their preferred brands, according to a marketing preference survey, personalization in survey questions is not all so common. A 2018 study, conducted by Evergage, shows that only 36% of surveyed businesses include a touch of personalization in their research efforts, in contrast to 71% utilizing personalized experiences in email messages.
Clearly, while still underutilized, personalized research surveys are gaining traction, and can bring many benefits. Given that recollections of past experiences are more often than not unreliable, and that many decisions are taken spontaneously, in-the-moment surveys offer companies the opportunity to evaluate customer satisfaction close to the moment of truth, when experiences are still fresh and untainted by bias. In-the-moment surveys provide a wealth and depth of data which is not likely to be reported in retrospective studies. When adding personalization to in-the-moment surveys, companies ensure higher customer response while collecting granular data, and sending out the message that they truly value customer feedback.
Trend #3: Merge research and data analytics
The abundance of customer data can easily cause companies to get lost in it, and miss out on the valuable insights it otherwise lures them with. However, according to Ivaylo Yorgov, Managing Director of Research at GemSeek, 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.
Neither behavioral data, nor demographic data alone are sufficient to help marketers and business managers understand consumer behavior and purchase patterns. The integration of behavioral data and survey responses is steadily becoming an imperative across industry. However, data blending is due in other aspects of customer experience management, as well.
Taking advantage of the full-range of data collection methods permits companies to profit from advanced analyses even when they do not initially possess great quantities of internal customer data. To that end the GemSeek approach is to unlock the value of internal databases through advanced data analytics, as well as enrich it by collecting relevant information from research and third-party data sources.
As we have written in previous articles, the fusion of multiple data sources turns research reports into insight reports with greater granularity. It also allows companies to gain familiarity with their client base at unrivalled scale, and to predict their attitudes and behaviour with great accuracy.
Trend #4: Predict customer behaviour
Being able to identify customers’ current behavior is without a doubt a cornerstone to enhancing offerings, sales and marketing efforts, and tackling ongoing challenges. However, in other to grow and set the pace in its industry, instead of merely following suit, champion-minded organizations need to acquire the much rarer skill of accurate prediction.
Predicting customer behavior is paramount in becoming an industry leader because it facilitates proactive customer acquisition and retention, and therefore ensures long-term customer loyalty and advocacy.
When customers resist to provide information as they mostly do, given that only an estimated 10% of clients respond to providers’ feedback surveys, customers’ likelihood to make a consecutive purchase or leave the business altogether can remain undiscoverable, unless digital footprint data is utilized to predict their behavior.
For instance, back in 2016 GemSeek partnered with one of the largest global logistics companies to predict which segments of their client base the company could upsell to. Our partner had over 60, 000 B2B clients solely in one of their key regions. They wanted their customers to spend more, but had a hard time distinguishing among them. The company would blast out messages to their whole customer base hoping to upsell, only to see their marketing spending drained and their customers leaving. To rectify this we built a predictive model to help our partners discover who are the most likely clients to buy and pinpointed the types of services most likely to interest them. The impact of this relatively small project was felt across the account management, marketing and sales departments within the organisation, and cost less than the lifetime value of a single customer.
This past December GemSeek won the Market Research Society (MRS) Award for Innovation in Data Analytics. Our innovative approach to customer retention through machine learning and predictive analytics carried out alongside our partner Liberty Global was selected among outstanding projects, incorporating Big Data from fellow industry players, including OmnicomMediaGroup and Twitter & IPG Mediabrands. In order to predict customer dissatisfaction and counteract customer churn, our project involved creating state-of-the-art machine learning algorithms using multiple data sets available to the company, including customer survey data, customer transactions and usage data, and demographics. Striving to create a practical solution to allow Liberty Global’s customer service representatives to pinpoint leaving customers and engage them proactively, we helped embed the predictive model into the operational system of the company. The model predicts customers at risk with 80% accuracy, allowing for operational effectiveness and improved retention management.
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