Are Data-Driven Insights Part of Your Growth Strategy?

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When an organization is pursuing growth and considering various strategic opportunities such as expansion into new geographies, new product development, diversification, etc., there is a lot at stake. Insights about consumers, markets, economies, geographies, and products prove extremely valuable and can make the difference between sustained success and failure. Yet, not all organizations are equally equipped to generate these insights. While most gather data, very few focus on analyzing it effectively and discovering insights that will guide growth strategy. 

What are data-driven insights?

When an organization uses a data-driven approach, it means that it makes strategic decisions based on data collection, analysis, and interpretations or insights. Such a data-driven approach enables companies to make more informed decisions, rather than depending on gut feelings and broad trends. In other words, using data helps companies create strategies and make decisions that are rooted in evidence. 

Let’s consider a business that we’re all familiar with: a food delivery mobile app. When you download the app, you allow it to collect different types of information about you — whether you use it or not. The app has access to your demographic profile, order history, contact information, etc. Now data analytical tools and techniques are put to work in order to understand and interpret user behavior. Soon, for instance, the analytical tool realizes that a particular customer has a preference for Italian food on Thursdays. Using this insight, you can tailor customized offers or updates about new Italian restaurants and push them to that particular customer every Thursday. This is an example of a data-driven insight.

What are the different types of growth strategies?

A growth strategy is a long term strategic position that helps an organization achieve a larger market share. There may be times when long term sales and profits are built at the cost of short term earnings, which requires a strategic view. 

The four most important kinds of growth strategies are

  • Diversification
    The strategy of growing your business by introducing new products to new or existing markets is known as diversification. It helps companies reduce risk by diversifying their portfolio of products. Apple adding phones to their consumer products is an example of diversification
  • Product Development
    The strategy of improving current products and services and introducing new versions of the same product is known as product development. This is a crucial strategy for businesses to retain their market share and enable continuous growth. Using the same example, Apple launching a sleeker Macbook air with a better processor and an improved display is product development.
  • Market Penetration
    Going deeper into your existing marketing to capture a larger market share is called market penetration. This requires focused marketing programs.
  • Market Development
    Selling existing products in new markets is called market expansion or development. Expanding to foreign or rural markets are some ways to achieve market expansion.

Data-driven insights are relevant and vital for each of the above growth strategies. They are an effective way of creating growth strategies simply because of how data works. The data that is collected by organizations is divided into different buckets by using a host of analytical tools and statistics. These groups of data help us identify trends and patterns amongst our customers. Once these trends have been recognized, they can help us in deciding which kind of growth strategy to use and what direction should be taken. 


A business can sometimes be completely disrupted by competitors with a radical new approach, possibly an innovative technology or business model. What are the options at this time? If the organization attempts to carry on as before, they may face drastic and irreversible loss of market share. On the other hand, business leaders can look at this as an opportunity to craft a new strategy that may involve entering new product categories.

Even if the business has not suffered such a drastic disruption, diversifying into new products or services could be an alternative that is considered for future business growth. Businesses can use customer data and buying behavior to understand preferences and predict what products they should add, change, or remove from their portfolio to suit the needs of their customers better. Also, when businesses derive insights that are based on data they are collecting, it gives them a clearer picture of how their business model might require changes to suit the evolving needs of the consumers. By applying sentiment analysis to unstructured data in social media and consumer surveys, businesses can predict which way the demand is shifting. 

Diversification requires investment as well as cash flow management as revenues may not scale up immediately. Data can help to predict the quantum of investment and for how long it may be needed. For instance, a cafe chain used data-driven insights from customer consumption patterns to identify that adding new flavors in iced coffee is the best product diversification step that they could take at this point.

Product Development

Using data-driven insights to improve current products — by adding, changing, or removing features — has been proven to enhance the probability of success. Data is not meant to be used for analysis conducted after an event has occurred. In fact, data is a proactive tool to identify customer needs in advance and make changes to products accordingly. To do this, companies need to invest in third-party intent data to create an informed road map for product updates. Product updates that have “ excitement” features, delivering a high sense of delight, are more likely to keep customers coming back and to gain new customers. 

Secondly, organizations need to be ahead of the curve by using data technologies like predictive analytics, AI and IoT data, where applicable. These technologies are crucial for helping businesses to predict the cost, efforts and impact of possible developments to a product.

Market Penetration

Using data to capture larger market share has grown exponentially in the past decade. Marketers firstly use various data sources such as big data from social media, CES (customer effort scores), NPS (Net Promoter Scores) to establish their marketing goals. For example, using NPS, you can recognize the causes of why customers may have a negative attitude towards your products and then create a campaign to change that image for those customers. Secondly, data can be used to identify which channels of marketing would work for your business. Lastly, with real-time data, marketers can identify the right timing for their marketing communications. This increases the response rate to marketing efforts. All these efforts eventually empower businesses to increase their market share and penetration.

Market Development

Finally, there are times when businesses need to find and expand to new markets for their existing products. This is done either by finding new uses for their products or identifying demand for their product with a different demographic. Venturing into a new market is a high risk, high return proposition, but this risk can be mitigated by using data. 

Firstly, the market size of a new market can be estimated using data from research or surveys. This can assist to predict whether it is viable to enter that market. Secondly, market data can help you identify possible new business opportunities. For example, if you offer transportation services, then you can look for new office or factory openings in the areas you already service, as these new openings will likely require transportation for their employees. 

Lastly, companies can leverage their internal data to identify where their traffic is coming from, and from what locations there has been a rise in traffic. Also, internal data can help businesses to find new micro-markets. Insights from this internal data can help companies tailor their strategy to enter those markets where they see growth potential. In the case of micro markets, for example, a high growth urban area with low competition could be deployed with extra sales reps. Similarly, a low growth urban area with intense competition could reduce the reps to save costs. The act of recognizing these pockets can be done using data. 

With the drastic effects of COVID 19, we may be seeing permanent changes in the structure of our societies and markets. From grocery shopping to doctor consultations and education, business models have undoubtedly been disrupted. The move to digital consumption patterns is creating unprecedented amounts of data. To stay viable and thrive in this rapidly changing environment, businesses need to anticipate what’s in store. Data-driven decisions are the answer.

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