Professional services firms are increasingly facing disruptive threats from data analytics solutions.
Consider, for example, new pricing analytics software that gives operating companies real-time access to revenue transparency – and no longer requires them to hire a team of management consultants for months to re-crunch their sales data in Excel. Or, take new predictive project analytics tools that allow leaders to proactively identify capital project risks – and change the way they leverage financial consultants and accountants.
How can professional services firms respond to the challenge – and opportunity – that these data analytics solutions present?
Their answer may very well determine their destiny.
Much is at stake. The professional services industry is comprised of more than 750,000 firms that generate over $1.3 trillion in revenue and employ nearly 8 million people in the U.S. alone. Innosight co-founder Clayton Christensen expands on this tectonic shift of business models for professional services firms in the October 2013 Harvard Business Review article Consulting on the Cusp of Disruption.
Many leaders at the helm of professional services firms are keenly aware of the opportunity to harness data analytics. But, whether they realize it or not, their traditional “solution shop” business model – one that employs teams of consultants using judgment to solve problems through a fee-for-service model – is distinctly different than the business model needed to deliver data analytics solutions.
Instead, these solutions require a “value-added business process” model that addresses clearly defined problems through repeatable processes. Clients can then be charged for output or usage of the solution.
In practice, professional services firms have taken two strategic approaches to building data analytics offerings. In one, firms have chosen to integrate data analytics into their existing, core business – often the “solution shop” model. In a second approach, other firms have chosen to design an entirely new business model outside their core.
Integrating data analytics into the core business by offering an analytics-enhanced version of the current offering can yield incremental, sustained sales to clients. For instance, take a consulting firm that is developing change management tracking software to complement their traditional consulting offering. The firm may choose to use the solution as a conduit to sell new consulting engagements – essentially to buffer their core business.
While firms that take this first approach to data analytics may sustain or grow revenues in the short-term, we find that companies who solely pursue this path often cannot fend off disruption in the long-term.
Designing the resources, processes and profit model separate from the core business and around the data analytics solution allows the company to grow the new venture without the biases of the core. Imagine that the firm described above has instead chosen to develop the software separate from the core. The separate entity would have an independent team for implementation and customer support. Sales would be incentivized to sell software (not consulting hours). And, most importantly, by measuring the profitability of the data analytics solution against this cost structure, the firm would be incentivized to continually invest in the solution and help it grow.
For leaders of professional services firms, it is not a choice between these two approaches but a consideration of how to pursue both approaches.
This strategy – in a broader context – is precisely what Clark Gilbert, Matt Eyring and Richard Foster refer to as a “dual transformation” in Two Routes to Resilience. They describe how companies, such as Xerox and the Deseret News, have managed to successfully stabilize their core offerings while independently incubating high-growth businesses.
In the example described above, the firm could offer the change management software to current clients for new leads to their core business, while simultaneously creating a suite of do-it-yourself change management software to serve new customers.
We recommend leaders adhere to the following principles as they take this dual-approach:
- Define clear boundaries between the existing business and the new offering. Too often, firms can effectively “kill” the data analytics solution if it is forced into the core business. Partners in professional services firms are often incentivized to sell large-scale consulting engagements and are not incentivized to sell cheaper, automated solutions. Where possible, firms can create different goals and performance metrics to distinguish between the existing business and the new data analytics solution.
- Establish a clear process for sharing resources between the core business and the data analytics offering. The new initiative will likely require the firm to acquire new capabilities (for instance, acquiring visualization tool licenses or hiring data scientists). These resources can be shared with the core business but only with well-defined processes, so that the data analytics solution is appropriately compensated.
- Allow the core business and data analytics offering to compete fairly in the market. The full disruptive potential of the new data analytics solution may be more or less apparent. But, the very fact that the separate data analytics solution competes in some capacity with the core can cause leaders to want to shield the core from cannibalization.
After all, leaders must be comfortable with a degree of cannibalization of the core. This strategy is what enables the new data analytics solution to emerge with its full disruptive growth potential.
Ryan Starks is an associate at Innosight.
Mariam Melikadze is an analyst at Innosight.