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Iterate, don’t transform, your customer experience

The rewards from major transformation projects can be significant, but the chances of success are slim. Metia’s Pete Morgan thinks an iterative approach to customer experience is a more reliable way for B2B marketers to increase returns.

In a recent survey of global businesses, management consultancy McKinsey found that 85 percent of organisations have embarked on digital transformation projects in the past five years. Success can have a material impact on the bottom line, with McKinsey reporting that companies who master digitisation are generating “eight percent more shareholder returns and a revenue compound annual growth rate (CAGR) that is five times greater than the rest of the field.”

The reality, however, is that all digital transformations are hard, and most are (as yet) relatively unsuccessful. Only 16 percent of respondents to McKinsey’s survey said their digital transformations had improved performance and equipped their organisation to sustain changes in the long term. Even in digitally savvy industries such as high tech, media and telecoms, the success rate did not exceed 26 percent. In more traditional industries such as oil and gas, automotive, infrastructure and pharmaceuticals, success rates fell by between four and 11 percent.

Organisations must resist the temptation to look inward

One of the most striking pieces of insight from McKinsey’s research was how organisations tend to look inward when attempting to transform, rather than focusing on customers. While 68 percent of respondents cited digitising their operating model as the primary objective, less than half claimed their objective was to launch new products or services or to interact with external partners. It’s speculation to suggest that brands see internal projects as low-hanging fruit, but it’s obvious that digital transformation projects will only deliver in the long term when they benefit both an organisation and its customers.

Running a project with an insular objective is unlikely to generate long-term value. One notable failure on this point was the UK government’s Driver and Vehicle Licensing Agency (DVLA), which saw a significant increase in untaxed vehicles after digitising vehicle licensing. Instead of generating an anticipated £7m annual saving in administration, the DVLA took a reported £94m hit to its annual revenue. In chasing efficiency savings through digitisation, the DVLA made tax easier to administrate – but undermined its commercial objectives as fewer customers went on to tax their vehicle.

Customer data provides a route to iteratively improve the buying experience

Rapid increases in the data consumers generate presents opportunity and risk for brands, particularly given recent privacy regulation such as the General Data Protection Regulation (GDPR) in the European Union and California Consumer Privacy Act (CCPA) in the United States. McKinsey estimates that B2B buyers now use six channels over the course of the average purchase.

Orchestrating data to create a consistent customer experience is difficult. Get it right and there is a significant upside for brands and consumers. Get it wrong and there is a substantial risk to reputation and customer relationships – not to mention the prospect of heavy fines. Standing still is not an option, however. Brands who ignore the growing pool of behavioral data risk falling into obscurity. In a study by consulting firm Accenture, 79 percent of executives surveyed believed ignoring big data would result in their company losing its competitive position and possibly even facing extinction.

A measured approach is often best, particularly in B2B where an organisation’s audience pool is often limited in size, and building and maintaining an emotional connection with customers is critical to a sale. Attempting wholesale change in these scenarios is risky and often unpalatable for the business – and this aligns with our knowledge that most large-scale transformation projects fail.

Instead, smart marketers are improving customer experiences iteratively, using analysis of behavioral data to create ideas to test on small samples of their audience. Successful ideas are quickly scaled across the business – but only when value is proven to both business and consumer.

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