AI and Analytics Opportunities
Everyone wants to understand how analytics and AI can be applied to help their business. Any quick review (or AI summary) from a web browser will broadly group AI-related opportunities into a few common themes: make better and quicker business decisions, improve customer satisfaction, decrease costs, and add (or improve) products and services. Unfortunately, most discussions end with these broad classifications and offer little in the way of deeper insight into how or why these technologies will offer your organization a competitive advantage.
Factoring in Competitive Advantage
One of the defining characteristics of a truly data-driven organization is that the business will align its analytics strategy with its strategic objectives. So how can we factor in strategic objectives to further improve and refine our discussion of opportunities for analytics and AI?
In 1980 economist and professor Michael Porter published his classic text “Competitive Strategy: Techniques for Analyzing Industries and Competitors” (Porter, M. E. Competitive Strategy: Techniques for Analyzing Industries and Competitors. New York: Free Press, 1980) and it is considered required reading for MBA and business students today. That work defines three strategies that enable us to refine where and how to deploy analytics and AI. Porter argues that competitive advantage is achieved by pursuing business strategies in cost leadership, differentiation, and focus:
If a company adopts a cost-leadership strategy, they have decided to act as a low (or lowest) cost producer in their industry while also maintaining high-quality services and products.
A differentiation strategy involves the creation of unique products and services (or a unique brand identity).
And finally, a focus strategy targets specific customer segments and meets that market’s unique needs.
If a company doesn’t commit to at least one of these three strategies, Porter warns the business will suffer from “stuck in the middle” syndrome and be highly at-risk from competitors with more focused strategies.
Five core elements are crucial when pursuing a selected competitive strategy: pricing, product features, customer service, target marketing, and branding:
Analytics + AI + Competitive Advantage = Success
Using this analysis, we’re now ready to reclassify our AI /analytics opportunities and to align for competitive advantage. Looking again at the broad categories for AI and analytics, we begin to put together a table such as this:
The first crucial step in evaluating your analytics approach is to identify which competitive advantage strategies (cost leadership, differentiation, or focus) your business uses. Then define specific goals tailored to those strategies. If your business is pursuing the cost leadership strategy, you now have a good framework for asking next-level deeper questions such as:
What decisions can I make more accurate and quicker (via analytics and AI) that will result in better customer pricing and overall support for our cost leadership strategy?
How can I cut costs (via analytics and AI) in a way that results in better customer pricing and overall support for our cost leadership strategy?
For example, in the freight brokerage business we all know that quick pricing decisions are crucial, and the strategic importance of having immediate access to current market rates is clear. But how can additional data-driven principles take this concept further? One potential answer is that a data-driven company can try to look more broadly than individual shipments and instead apply analytics and experimental design principles to fine-tune margin results across multiple customers and lanes. As differing pricing strategies are tried, a data-driven organization will capture and analyze the results of pricing experiment so that a company’s total revenue is optimized.
Minimize the Cost of Analytics
Always remember, the cost of implementing an analytics strategy needs to be in line with a measurable improvement to revenue. Consider best practices on how to actually measure the ROI of analytics. Cost-effective analytics solutions are especially crucial for any businesses following a cost-leadership strategy. While some up-front investment in analytics is to be expected, beware of the large price tag associated with data science and information technology infrastructure, or long-term estimates for positive ROI. If you are just starting out with analytics and your first target project won't see a return in 3 to 6 months, consider alternative projects that give better opportunities to learn and grow.
Conclusion
Decisions about how to deploy analytics, and what kind of results to expect, can be complicated. But classic research into competitive advantage, and the related strategies of cost leadership, differentiation, and focus, offers us insights about how to ask better questions about how we deliver analytics that are actually important to the business. If you align your analytic initiatives with your business objectives, and you focus on supporting the specific competitive-advantage strategies adopted by your organization, then you can find a high degree of success and maximize the ROI for your analytics & AI investment.