Building a competitive advantage through analytics

Winning on the chosen playing field by leveraging data and analytics

Winning is “being successful in achieving or getting something that others also wanted” (Cambridge Dictionary). The desire to win is embedded in our nature and has been an important driver in human evolution. For companies, winning is critical to survive and remain relevant in the competitive environment. Companies are able to win in the marketplace if they “provide a better consumer and customer value equation than competitors, and can do so on a sustainable basis” (Playing To Win, Roger Martin). If companies do not make conscious choices to enable themselves to win, they are unlikely to establish a sustainable competitive advantage. Without a sustainable competitive advantage, they are left to the mercy of market forces – resulting in mediocre performance at best, and bankruptcy at worst.

In the previous blog, we discussed the concept of strategic precision: defining highly granular micro-pockets, on multiple strategic dimensions, where competitive advantage and profit can be maximised. This strategic precision should be coupled with a clear ‘smart engagement’ choice to activate the intended competitive advantage in order to maximise profit. Applying advanced analytics algorithms on extensive datasets of customers, competitors, products and the value chain can provide the required insights to gain a competitive advantage through differentiation or cost leadership. A well-known example is Walmart using extensive data capabilities to reinforce its everyday low price (EDLP) strategy. By combining sales data with external data and using advanced algorithms, Walmart is able to predict demand in granular micro-pockets. Walmart leverages these insights to adjust its inventory and customer service levels at specific points in time, thereby better addressing customer needs and lowering its cost base.

Improving the customer value proposition by using the power of data

Strategic theory identifies two ways to obtain a competitive advantage: differentiation or cost leadership. Differentiation strategies focus on “offering products or services that are perceived to be distinctively more valuable to customers than are competitive offerings, at a similar cost structure” (Playing To Win, Roger Martin). Applying advanced analytics to internal (e.g. sales data) and external data (e.g. micro-economic data) enables companies to create a deeper understanding of customer needs and the competitive playing field. This can allow differentiators to define a stronger value proposition for their customers (by adjusting their brand, services, quality, etc). Cost-leadership strategies focus on offering similar products or services as competitors at lower cost structures. Applying analytics to value chain or product lifecycle data can enable companies to better identify improvement opportunities in sourcing, design, production and distribution, thereby driving cost leadership.

In the previous blog, we mentioned our GrowthPath® methodology, which can be used to define market growth opportunities and prioritise customer segments. Our Strategy Analytics team also uses the GrowthPath® methodology to define smart engagement with customers and against the competition by analysing customer behaviours, competitive positioning, product offerings and activation strategies.

Data and analytics have become major assets for companies to leverage into a superior strategic position through differentiation or cost leadership. An example that speaks to mind is Netflix, the on-demand video content provider that chose to tackle the market through differentiation. Most of us grew up entertaining ourselves by watching television, regularly switching channels in search of something fun to watch. Netflix chose to change this field of video entertainment using vast amounts of data and recommendation algorithms. Next to disrupting the market by moving into a digital-only medium, Netflix applies data and analytics to deliver a customised product to its subscribers. Through pattern recognition and segmentation, Netflix aims to get to know its customers and provide tailored content to specific customer segments. At first, Netflix segmented its customers based on geography and demographics, but as its database grew, the company switched to more advanced models. Using millions of rows of data, customers are clustered based on their preferred content, and receive recommendations related to their specific cluster (or “taste community” as the company refers to it). Applying advanced algorithms – for example, K-Means Clustering – enables Netflix to identify previously unknown clusters, and new subscribers can be added to those clusters using the classification techniques such as K-Nearest Neighbors. Currently, Netflix has over 2000 different taste communities, enabling the company to deliver a highly personalised experience, differentiating itself from traditional video entertainment companies.

Data and analytics can also play a vital role in lowering cost structures to build a cost leadership advantage. For example, we recently applied analytics to create a proof of concept for optimising the workforce planning cycle of a client. Based on multiple years of historical data, a demand forecasting model was developed. We enriched the data with external factors such as holidays and a seasonality index to create a predictive machine learning model. The model provides insights into how demand moves over time, covering different scenarios. The demand forecasts, employee preferences and business logic are combined in a multiple constraint model to optimise and automate the rostering process. This analytical solution enables the company to more efficiently and accurately manage its workforce planning cycle, thereby improving its cost leadership position.

Making data-driven strategic decisions to win in the market

In this blog, we focused on the value of using data to establish a sustainable competitive advantage, by making ‘smart engagement’ choices. Our key assertion is that data-driven strategic decision making enables companies to create a winning value proposition, driving above industry average growth for a business.

In our next blog, we will take a look at some of the key steps for defining and executing a data-driven strategic decision process.