The Business Pivot Opportunities Created by your Data

Background

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Many companies focus on using the data that they currently possess to support the existing businesses. This is, of course, the correct thing to do because the effects of such efforts become quickly obvious. If a program to actively harness data analytics into the current decision-making process results in an observable tangible increase in profits, management can easily quantify the contribution and justify putting more resources into the program. Furthermore, the increase in profits can provide for money to put into the transformation.

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Companies often ask the question, "How can I monetize my data better?" This is a perfectly valid and important question to ask, and often the first answer that comes to mind is "Who can I sell the data to?". Selling the data (obviously subject to various legal restrictions on customer privacy) is a quick and easy way of monetizing the data, but it is not the only way, and may not necessarily be the best way.

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What seems to be often much less appreciated is the hidden potential of your data to launch new businesses or to allow your company to pivot in a new direction to a market that has greater potential.

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The Idea of a Career Pivot

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The concept of a 'pivot' is often associated with career changes. For example, a 45-year-old marketing executive who has been working in the fertilizer manufacturing industry for 20 years might one day decide that he is tired of the job and wants something different. At his age and with his set of financial obligations, it is not feasible for him to try something totally different, like trying to become an AI software engineer when the most advanced computer skill that he possesses is using Excel to create monthly financial sales reports.

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Most career counsellors will recommend a 'career pivot', where he tries to find a job in a different industry (so that it satisfies his desire of 'something different') but allows him to leverage the various skills that he had assiduously cultivated over his 20-year-career in marketing (for example, negotiation skills and people skills). One option for him could be to become a career coach for other executives, focusing on teaching negotiation and people skills, in various industries.

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At the heart of the decision-making are two key questions: "What are the skills that I am good at and I want to keep using?" and "Where are the new career opportunities?"

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The Traditional Business Pivot

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The business pivot is essentially identical in nature to a career pivot. The two key questions that any company contemplating a pivot will need to ask are: "What are the skills and resources that my team and business currently have?" and "Where are the new business opportunities?"

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In answering these questions, the answer to the skills question rests on the skills of the current staff, and the resources rests on the current assets owned by the company. For example, during the Covid pandemic, many restaurants pivoted to selling takeaway meals because market demand shifted (from dine-in to takeout) and what restaurants were good at was cooking food. It did not take much effort to shift from preparing a meal for dine-in to preparing it for take-out. The production line had to change a bit, starting from order-taking (some restaurants needed to create a team to take orders over the phone or online) to packing (restaurants needed to figure out how to package dishes in order to best retain the flavor) to delivery. There were changes required, but the new takeout business still leveraged its core dine-in capabilities.

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In the modern data-driven world, this 'traditional' way of answering the business pivot question is still perfectly valid in many cases. However, we want to propose a new way of answering the question of what business to pivot to and how to pivot.

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The Data-driven Business Pivot

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The data driven business pivot offers a slightly different perspective. When we speak of a 'Data-Driven Business Pivot', there are 2 possible interpretations:

1. Using data from the existing business to decide which direction to pivot to

2. Taking a fresh look at the data assets that the company currently has and look for business opportunities where the data will confer a competitive advantage.

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Interpretation (1) is a more widely used approach. The company looks at the data that is gathered through its daily operations, examines which are the areas that they are doing well in and which areas they are weaker in, and decide if they should reallocate resources from the latter to the former.

 

Interpretation (2) is what we would to elaborate upon here. Disregarding whatever core businesses the company currently has, it takes an open-minded look at the question "What business opportunity can be generated by the data that we currently have?" Based on that answer, the company can then decide on whether it has the capability to exploit that opportunity or not. If it does, this could be a totally new business for it to venture into.

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What does our Data-driven Business Pivot entail?

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Firstly, we have to recognize data as an asset. A complete, up-to-date and well-analyzed database is a very valuable asset. For example, a large and good customer database allows a company to know its customers very well, perhaps including what products and services they like, how they want these services to be delivered and priced etc. It also provides the company with the means to contact and market to these customers.

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Secondly, we have to think laterally and creatively on how we can use the data, to turn the data into a competitive advantage. For example, online retailers like Amazon can use their detailed customer database to basically market any product that Amazon carriers to them. When Amazon entered the fresh foods business, they could use the existing online retail consumer database to market Whole Foods to them. A company that specializes in personal shopping for high-net-worth individuals will likely have a database on what products and services appeal to such individuals and what makes them want to purchase such goods and services. They can use this data to be a marketing consultant to companies that want to launch new products to such high-net-worth customers - the product companies have the product but do not know what drives the purchasing decisions of high-net-worth customers, while the personal shopper knows these purchasing decisions inside-out.

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Thirdly, we can think more expansively about the skill sets of the team members that we need to launch the new business pivot. In the pre-data-driven world, the skill sets required were often more narrowly defined and specific. For example, if your business wanted to market products to high-net-worth individuals, you needed to hire people who had sold other products to high-net-worth individuals because all the specific knowledge was locked in the brains of these people. In the new data-driven world, a lot of the specific knowledge might already be kept in the database and unlocked with the right analytics. You are now no longer restricted to hiring people with that narrow experience but can cast the net wider. In fact, almost anyone with experience in marketing high quality products (assuming high-net-worth-individuals like only high-quality products), armed with the knowledge about the purchasing decision-making process of high-net-worth-individuals gleaned from the database, can probably do as good a job.

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Finally, we need to ensure that we have people on the team who know how to analyze and interpret the data from the database. Analyzing data is relatively straightforward because there are many standard procedures and tools that make analysis fast and easy. Interpreting data is a much more difficult task because it often requires a lot of industry-specific or situation-specific knowledge and experience to draw the right conclusions.

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The biggest difference value of the data base is that a lot of the knowledge is institutionalized within the firm. Previously, a lot of knowledge was kept in the brains of the employees. Since the advent of modern databases, there has been a long trend of the institutionalization of knowledge. For example, a CRM like Salesforce collects and stores all the information about every customer for the firm, and the firm owns this information. Before this, the majority of customer information was kept in the mind of the salesperson or his notebook, and the salesperson often owned the customer relationship. When the salesperson left, a lot of the knowledge about the customer left with him. Today, with CRM, when the salesperson leaves, the knowledge about the customer still remains at the firm.

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Just like a CRM institutionalizes knowledge, any database will also institutionalize knowledge. (A CRM is simply a very specialized database.)