Investors and executives alike believe that using customer data to create a competitive advantage is possible. You can collect more data the more clients you have.
This data is then analyzed using machine learning tools to create a product that will attract more customers. Then you can collect more data to marginalize competitors, just like businesses that have a large network effect. So the theory goes.
This assumption is often wrong. Most people vastly underestimate the value of data.
Data-enabled cycles may resemble regular network effects. An offering, such as a social media site, becomes more valuable the more users use it.
In practice, however, regular network effects tend to last longer and be stronger. For the best competitive advantage, you will need as well as data-enabled learning. Few companies can do both. Under the right circumstances, customer-generated information can still help build your competitive defenses even when network effects do not exist.
Well explain in this article what these conditions are and how you can evaluate them to see if they apply to your company.
Build Moats With Data-Enabled Learning
Companies should ask themselves seven questions to determine the sustainability of a data-enabled competitive advantage.
What Is The Value Added To An Offering By Using Customer Data?
It is more likely that the higher the added value, the longer it can last. It is the leader in advanced driver assistance systems, including collision prevention and lane departure warnings, systems are primarily sold to auto manufacturers, who test them thoroughly before they incorporate them into their products.
The systems must be reliable and accurate.
What Is The Value Of Learning Based On Data?
How soon will the company arrive at a stage where the additional data about customers no longer adds value to an offer? The stronger the barrier, the slower the marginal value declines.
When answering this question, you should measure the value of learning by the customers willingness to pay and not some application-specific measures, such as the percentage of chatbot queries answered correctly or the number of times movie recommendations were clicked.
If you plotted the accuracy against customer usage (total mileage driven by auto manufacturers who tested it) and discovered that a small number of manufacturers, and a medium level of testing, would suffice to reach, say, 90 percent accuracy, but that a larger group of manufacturers, and a greater amount of testing, would be required to reach 99% accuracy and 99.99% accuracy.
It would be wrong to interpret that as meaning that the marginal value of customer data was decreasing rapidly.
A 9 percent-point improvement in accuracy is still a very valuable addition, especially when it comes to life-and-death situations.
Even the biggest car manufacturers would find it difficult to generate enough data or replicate data, dominant position marketplace made it a very attractive purchase for Intel, which paid $15 billion for it.
Products and services that have a high marginal value from learning from customers data, even when a large number of them have been acquired, are competitively advantageous.
This is evident in systems that predict rare diseases, such as those provided. Microsofts Bing has failed to challenge Google in the search market, despite spending billions and years on Bing. For search engines to deliver consistently accurate results, they need a large amount of data from users.
How Quickly Does The Value Of User Data Diminish?
All other factors being equal, if the data is quickly outdated, then its easier for the rival to enter the marketplace, as it wont have to learn from the same data that the incumbent has been using over the years.
Current products still benefit from all the information it has collected over the years about car manufacturers.
Googles data over the years on users of search engines is also valuable. There is no doubt that having historical data on searches over the years can be very valuable in helping users today.
The low rate of depreciation in their data helps to explain why, as well as Google Search are both very resilient companies.
The value of user data in casual social games on computers and mobile devices tends to diminish quickly. The company is known for heavily relying on analytics of user data to inform design decisions.
However, the lessons learned in one game didnt translate well into another. Casual social games can be subject to fads, and users preferences change quickly.
Does The Data Have A Proprietary Status, Meaning That It Cannot Be Bought From Another Source, Copied Easily, Or Reverse-Engineered?
To create a defensible barrier, its important to have unique data about customers. It offers a system for crop management, allowing growers to monitor their plants continuously.
This system uses computer vision software and AI to monitor plant biometrics that are not visible to the naked eye.
The Big data strategy is then translated into valuable insights for growers to use to improve their yields, prevent diseases and reduce outbreaks.
a variety of factors, including agricultural conditions and variants, the larger the number of growers it serves. This will increase the accuracy of the predictions made for existing and new customers. Compare its position with spam-filter companies, which can easily acquire the users data.
This helps to explain why there are dozens of these providers.
Keep in mind that technology can undermine positions based on proprietary or unique data. Speech-recognition programs are a good example.
In the past, software had to be trained to recognize the individual voice and patterns of speech. The more the user used the program, the better it got.
In the last decade, speaker-independent systems have improved rapidly. They can now be trained using publicly-available sets of data, and it takes little or no time for them to pick up a speakers accent.
The advances in speech recognition have enabled many companies to offer new applications, such as automated customer service on the telephone, meeting transcription services, and virtual assistants.
Is It Difficult To Replicate Product Improvements Based On Data From Customers?
Its hard to create a sustainable competitive advantage when competitors can copy the enhancements resulting from the unique data.
It depends on a few factors whether companies can overcome this problem. The first is whether or not the improvements have been hidden in complex processes, which makes them difficult to reproduce.
The firm used its proprietary Music Genome Project to categorize millions of songs based on 450 different attributes.
This customization is not easily copied by competitors because the Music Genome Project has a strong connection to it. Contrary to this, design improvements that are based on the learnings from customer usage of office productivity software can be observed and easily copied.
This is why dozens of software companies offer the same type of product.
Second is the speed at which insights from data about customers change. They are harder to copy the faster they change.
Many design elements of Google Maps interface are easily copied by other companies (including Apple Maps). Only those companies can replicate this feature. Apple Maps has closed the gap between Google Maps and Apple Maps. However, this is not the case in other countries with a smaller user base.
Can The Information From A Single User Be Used To Improve The Product?
Idealistically, the firm will be able to do both. However, the differences between them are important. The firm may customize the product based on the data of a single user, thereby creating switching costs.
If data collected from one customer is used to improve the product, it can create network effects.
The two types of improvements help create a barrier to entry. However, the first makes current users very loyal, while the second gives a competitive advantage to newly acquired clients, where it enjoys a loyal.
Customization based on data from individual users helps to keep customers loyal but doesnt produce the same type of exponential growth as network effects.
How Quickly Can Insights Derived From Data Collected By Users Be Integrated Into Products?
Competitors are unable to keep up with rapid learning cycles, particularly if several product improvement cycles take place during the contract of an average customer.
When it takes several years, or even successive generations of products, to improve the product based on data collected from users, the competitors are more likely to be innovative and collect their data.
The competitive advantage of customer data increases when the learnings from the current users translate into frequent improvements to the product rather than only for the future users of the service or product.
Maps, crop management systems, and search engines are just a few examples of products that can be updated quickly to include feedback from customers.
Direct online lenders such as LendUp or LendingPoint offer a counterexample. They learn to make better decisions by studying the repayment histories of their users and how they correlate with different aspects of profiles and behaviors.
Existing users can only benefit from the learnings of prior users, as this is already reflected in the rates and contracts that are being offered to current borrowers.
Since their current contracts will not be affected, there is no need for the borrowers to worry about future learnings that may benefit lenders.
Customers dont care how many other customers sign up for a lender when they decide whether or not to get a loan. Customers may prefer to stay with the lender they know best, as this is the one who knows them well. However, many lenders will offer loans.
Do Data Have Network Effects
Customers will be interested in how many people adopt a product if the learnings from one customer translate into an improved experience for others.
This mechanism is similar to that which underlies network effects on online platforms. Platform users join larger networks not to gain more insight into products but because they prefer more interaction with others.
Google Maps is worth a second look. The software is used by drivers because they are expecting others to use it as well.
And the more data it collects, the better it can predict road conditions and travel time. Google Search, AI-based system for crop management, and other data-driven network effects are also enjoyed by Google Search.
Data-enabled network effects can also create entry barriers. The cold start challenge is the same for both types of network effects: businesses wishing to create regular network effects must attract a minimum number of users in order to begin the effect.
Those wishing to achieve data-enabled network effects also need a certain initial data volume to initiate the learning cycle.
Although they are similar, data-enabled and regular network effects differ in important ways. They tend to strengthen the advantages of regular network effects.
Data-enabled effects are less likely to suffer from the cold start problem, as buying data is much easier than purchasing customers. Alternative data sources, even when not perfect, are often able to level the field by eliminating the requirement for large customer bases.
To produce data-enabled, lasting network effects, a firm must constantly learn from the data of its customers, which benefit from regular network effects get better even while I am sleeping." Regular network effects are interactions between users (and with complementary third-party vendors) creating value, regardless of whether the platform is innovating.
Facebook has a powerful network effect. Even if the new social network had better features (for example, privacy protection), users would want to stay on Facebook.
In many instances, learning from data about customers can be done with a relatively small number of clients. In some cases (such as speech recognition), AI improvements will make it possible to reduce the requirement for data from customers so that the data-enabled benefits of learning may disappear entirely.
The regular network effect, however, is more robust and extends further. An extra customer can still enhance value to existing customers, even if the total number of customers in the market has already reached a large level.
Read More: Advantages of Big Data Automation for a Data-Driven Business
Ways to Leverage Data for a Competitive Advantage in Marketing
Marketing has traditionally relied on intuition and observation. While this "spray-and-pray" method was effective for a while, it wasnt scientific or systematic.
As the data increased, the demands of consumers also grew. These evolving consumer needs have placed pressure on businesses. Most online consumers are often frustrated by content that doesnt even come close to matching their interests.
This can be on social media or in the mail. They will not even engage or read the content if they dont find it interesting.
In a highly competitive environment, it is important to adopt a systematic marketing approach. It is important to understand your customer as much as possible and create strategies and campaigns which are tested against other approaches.
In todays world of data, there is little room for intuition. We have all kinds of metrics that we can measure and improve. Businesses that understand this have a two-step advantage in terms of their results and efforts.
Data-driven big data companies have existed for many years.
Consider the credit bureaus. These companies have significant barriers to entry due to the scale economies involved with acquiring and structuring large amounts of data.
However, their business models do not involve mining data to understand customers better.
It is a tried-and-true strategy to gather customer data and use it to improve products and services. However, the process was slow and limited, making it difficult to scale.
For automakers, consumer-packaged-goods companies, and many other traditional manufacturers, it required crunching sales data, conducting customer surveys, and holding focus groups. The sales data was not always linked to specific customers, and surveys were time-consuming and expensive.
The cloud and the new technology that allows firms to process vast quantities of data quickly and efficiently have changed the game.
The Internet can collect data on users, such as their search history, content preferences, communication, social media postings, GPS location, and usage patterns. Machine-learning algorithms can analyze "digital waste" and automatically adjust a business offerings to match the results.
Data-driven learning is now much more powerful and effective than any customer insight that companies have produced before. However, they do not guarantee that barriers will be defended.
Data As An Advantage In The Competitive Market
The importance of using data for improving all processes, including sales and marketing, increased as the methods of collecting data developed.
The more information a business has about its clients and prospects, the easier it could tweak its marketing efforts to achieve better results. How leading marketing companies are using data to achieve a competitive edge.
Personalizing Customer Experience
Marketing is a great place to use personalization. Personalizing your content means tailoring it to their preferences, their online activity, their purchase history, their browsing history, and any other relevant information, created a marketing campaign to target people who have recently relocated.
The company knew from the data that they collected that customers who had moved into a new area were more likely to use their services.
The company combined records from the U.S. Postal Service of applications for change-of address and made personalized homepages, which only relevant individuals would be able to see.
The standard website had a lower conversion rate.
Coordinating Marketing Initiatives across Channels
Identity resolution is a common first step in an omnichannel marketing approach. Identity resolution is the process of recognizing an entity, whether its a person or place, and its associated relationships based on their physical and digital characteristics.
Identity resolution is used to plan omnichannel campaigns based on a users hobbies, interests, and digital footprint.
Building User Profiles Using Predictive Analytics
Businesses have integrated predictive analytics with account-based marketing in order to focus on accounts that closely match the Ideal Customer Profile.
The ICP, or ideal customer profile, allows the marketing and sales units of a company to work together to find the most qualified leads in the sales funnel.
Read More: All You Need To Know About Big Data
Tracking And Increasing Marketing Roi
To get the most out of the growing amount of data, it is important to combine analytics and the ability to communicate findings.
According to research, businesses realize an average ROI of 1,401% for each dollar they spend on business intelligence and analytics solutions, ways are used by businesses to track and increase marketing ROI using Big Data technologies.
Breaking up independent silos in order to improve data flow throughout an organization. Data should be easily integrated with other systems and shared with both internal and external parties, such as affiliate marketers and internal SEO teams.
Assuring that the data streams update in real-time to allow quick actions based on accurate info. Analyzing such data streams allows marketers to compare the performance of past campaigns with that of current campaigns and discover new business models or opportunities.
Visualizations can be used to make complex information more understandable. It is also useful when discussing analysis results and brainstorming future marketing campaigns.
Smart experiments are conducted based on various marketing strategies to gain insight and find alternatives. Data-driven tools are used to base all marketing decisions on previous customer data. This allows you to predict future obstacles and patterns.
It also helps determine how best to mitigate risk.
Data Onboarding Converts Offline Formats Of Data Into Digital
Onboarding data can be defined as the conversion of offline information, such as postal and telephone addresses, purchase history, etc.
Data onboarding is the conversion of offline data - such as telephone numbers, postal addresses, purchases, etc. - to digital formats in order to support marketing campaigns. Businesses can make more informed decisions by integrating offline data on online platforms.
Data-Driven Marketing Increases Success Rates Statistically
Its no surprise that 78% of organizations that were surveyed in a recent study chose data-driven marketing, stating that this method increased customer acquisition and conversion.
Another study revealed that 67% of marketing leaders saw increased rates of customer acquisition after using data-driven marketing.
Are You Data-Driven Yet?
Marketing is a science in todays competitive, digital world. And as with any other science, you constantly need to keep forming hypotheses, testing them, and revising/modifying/accepting them as per the results of your tests.
With so much customer data available, testing hypotheses and accepting or rejecting them is easy. The first step, however, is accepting that the ever-growing pile of data has a great deal of potential and value. Youll soon be well on your way toward becoming data-driven with all of your endeavors.
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Conclusion
Data-enabled data will become more important as even the most mundane products are becoming smarter and more connected.
For example, new clothing can track vital signs and mileage and react to changes in weather. Big Data solutions will be used to enhance and personalize more and more offerings as even mundane consumer products become smart and connected, new kinds of clothing can now react to weather conditions and track mileage and vital signs.
In decades to come, the ability to improve offerings using customer data will become a requirement for remaining competitive.
This may also give existing players an advantage over newcomers. In most cases, however, it wont create a winner-takes-all dynamic.
