The future of tech buying growth and retention
As technology continues to become more critical to the business, technology customers have access to more options and information than ever before leading to more instances of buyer remorse. 56 per cent of organizations said they had a high degree of purchase regret over their largest tech-related purchase in the last two years, according to a new survey by Gartner, Inc.
In November and December 2021, Gartner surveyed 1,120 respondents in North America, Western Europe, and Asia/Pacific to understand how organizations approach large-scale buying efforts for enterprise technology. Respondents were required to be at a manager level or higher, aware of large-scale buying efforts for technology occurring during the past two years, and directly involved in the evaluation or selection of products or services for technology projects.
“The high regret feelings are at their peak for tech buyers that have not started implementation, indicating significant frustration with the buying experience,” said Hank Barnes, distinguished VP analyst at Gartner. “In the past, it was relatively easy for product leaders to predict who buyers were, but no longer. Buying team dynamics are changing and customers can find buying to be a real challenge.”
Barnes identified key changes in tech buying behavior during the Opening Keynote of the Gartner Tech Growth & Innovation Conference 2022, taking place virtually this week.
“There can be significant downside to regret associated with enterprise technology decisions. The survey found that the organizations that indicated they had high regret for their purchase took, on average, 7 to 10 months longer to complete that purchase,” said Barnes. “Slow purchase decisions can lead to frustrated teams, wasted time and resources and even, potentially, slower growth for the company.”
According to the survey, 67 per cent of people involved in technology-buying decisions are not in IT which means that anyone could be a tech buyer for their organization. In this environment, a new technology adoption chasm is emerging. This new chasm divides organizations that are confident adopters and buyers of technology from the vast majority that are not. High-tech providers need new approaches to identify and engage these different types of B2B customers and predict which type of customer they are dealing with to improve the odds of winning good business.
“To shift strategies, we need to think about psychographics beyond the motivations for buying to also include how decisions are approached and which groups are driving the strategy,” said Barnes. “Gartner has developed a psychographic model called Enterprise Technology Adoption Profiles (ETAs) that revealed seven specific customer segments. Using ETAs is one element that can help high tech providers move from a product/market fit strategy towards a product/customer fit strategy.”
Additionally, high tech providers should create a model to help identify “best fit” situations and “should avoid” situations. “Best fit” situations should be captured in an ideal customer profile – an enterprise persona – which focuses on the characteristics of the organizations being targeted, not the individuals within those organizations. It can include a variety of factors including the technologies they use, their business situation, the resources available to them and psychographic ETAs.
“There will be a big grey area in between that you have to be thoughtful in evaluating whether to commit to pursuing the opportunity. This is all about improving your odds and allocating resources and investments effectively,” said Barnes.
Having a keen understanding of the ideal customers will help high tech providers shape their strategies. With this insight, Gartner recommends that high tech providers do three things:
Focus the bulk of investments and effort toward supporting the “best fit” situations with the right offering, the right messaging, and the right type of content and engagement activities.
Train customer-facing teams on how to recognize the customer characteristics that indicate a “best fit.”
Train customer-facing teams on how to adjust their approach when encountering prospects that fall into the grey area between “best fit” and “should avoid.”
