Introduction

Data is the new oil/new currency. Equipped with it, upcoming organizations are disrupting established industries, and traditional businesses are transforming the way they operate. Not all organizations are equally proficient at translating data into currency, but their ability to do so is impacting their ability to compete.

"Where knowledge is power, data is wealth. It's not essential in the data, it's what you do with it," - Bruce Daley, an analyst at market intelligence firm Tractica and author of Where Data Is Wealth: Profiting from Data Storage in a Digital Society.

  Organizations that are most progressive in thinking about data differently are the organizations that are changing the economy,like Google, Facebook, Uber etc. Most businesses lag way behind in terms of the idea that data could be their primary reason for being. Enterprises today compete on their ability to find new opportunities and discover new possibilities.  Organizations competitiveness and differentiation lies in their ability to leverage data and analytics.

As per Markets and Markets report, Data monetization market is expected to grow from US$ 1.42 Billion in 2018 to US$ 3.12 Billion by 2023, at a CAGR of 17.1% during the forecast period, due to the growing usage of external data sources, advanced analytics, and visualization techniques to make insightful decisions from a large pool of data. In addition, the increasing volume and variety of business data is expected to contribute to the growth of the market. Moreover, enterprises consider data monetization tool as an important part of creating insights from a large volume of data to improve the operational efficiency of organizations.

Today most of the organizations experiment with unique ways to monetize the value of the data they accumulate during their operations. A few business model examples include:

• Maximizing customer loyalty and retention, by studying insights from customer data available in business support systems like CRM, help desk and other front-end applications
• Launching enriched products, value-added services, innovative rate cards and combo offerings that provide access to untapped and underserved segments
• Empowering customer care agents and sales teams to upsell and/or cross-sell based on existing and predictive consumption trends.

In today’s hyper-competitive market environment, optimizing cost and delivering superior user experience have been the top two priorities across the world to achieve differentiation and drive profitability. An effective data monetization strategy recognizes data as a growing asset living and mounting within a living enterprise and NOT as a stationary asset that depreciates in value. While there are promising benefits of Data Monetization enterprises must negotiate overwhelming challenges in their process to build these assets. Some of the challenges include:

• Understanding your customers better: To improve service, respond to their needs anywhere, anytime and across any channel.
• Data Accessibility: Access to quality data in secure and self-service way and in silos on multiple in-house and cloud environments. Challenge of getting the right data, in a format that is consistent and useful.
• Data Acquisition: Acquiring and processing diversified huge data sets made available from sensors, events, etc. in real-time.
• Data Cleansing is a never-ending challenge that will exist if an enterprise creates new data or essentially if it exists. This is a challenge that is even more articulated in the era of unstructured data. Getting insightful signals begins with the quality of the data. However, the reality is that we don’t control all the data sources and data tends to get contaminated along the way.
• Regulatory needs: Fulfilling regulatory needs that are continuously changing for greater transparency in data submissions and faster response to regulators queries (Eg: GDPR, CCPA etc)
• Delivery: Delivering accurate and actionable insights to business users at any time to improve operational efficiency.
• Data overload and having data that is too widely distributed are common barriers to data monetization initiatives leading to compromise on possible competitive opportunities.
• Data scalability is critical not only from collection and storage (warehousing) perspective, but also from speed of digestion and processing, access and security, as it is gathered, stored, consumed and finally delivery to end user customers (internal and external). Challenges that are real and grow as data gets bigger (volume) and more complex (variety and velocity).

We will never ever have perfect data, complete data, enough time to be exhaustive, or the perfect monetization model. Although a plan for data monetization may be revolutionary, execution should be evolutionary and focused on incremental value creation guided by an overarching data monetization strategy. In short, technology and its inherent and ever-evolving challenges may influence our perceived capabilities but should not define our strategies and the dimensions of the competitive advantage we can gain through data monetization. Data monetization is a strategic business initiative and not an IT project.

Challenges of Data Driven Business in FMCG & Retail

Retail Challenges of Distribution and Potential Data Applications

Although retailers were distributing products passively in the past, with the information about the end customer demand, they are becoming more proactive in the supply chain. The retail supply chain contains four major activities: assorting goods, breaking goods into smaller packages, holding inventory near the customer, and providing value-adding services such as gift wrapping and warranties. A comprehensive set of planning processes include product design (private label merchandise) and assortment planning, sourcing and vendor selection, logistics planning, distribution planning and inventory management, clearance and markdown optimization, and cross-channel optimization.

Major activities of retailing are to decide what products to carry (assortment, product design, procurement), how to sell products to customers (marketing, including pricing), and how to complete these efficiently (supportive functions, such as logistic planning).

Out of stock (OOS) situations and poor on shelf availability (OSA) lead to customer dissatisfaction and consequently loss of market share. Assortment, pricing, and store layout are challenging processes in retail operations that are expected to benefit from data analysis. Assortment and pricing involve a large amount of granular decisions and can be affected by various factors, such as customer preferences, store location, and demand elasticity. Without effective quantitative analysis, making these decisions can be resource- consuming and ineffective. Store layout is also worth exploring as it can affect customers’ purchasing decisions.

CPG Challenges of Distribution and Potential Data Applications

Distributor data is valuable to suppliers for product development, market planning, and new customer acquisition. Suppliers do not have visibility on the end use of their products when customers are served via distributors. They don’t have insight into which customers buy what, when, or why – all critical information that could help to both drive growth and efficiency directly within their customer base.

Additionally, suppliers are largely flying blind when it comes to benchmarking performance versus other suppliers and determining where they are best in class and where they should focus improvement efforts. Access to robust, rich comparative data enables step-changes in organizational performance.

Business Models for Data Monetization

Many organizations use its internal performance data, and at times triangulate with external demographics information, to create a competitive advantage for the enterprise, termed as “Return on Advantage / Competitive Advantage Model”. It helps in risk mitigation, fraud detection & customer targeting. Organizations use purchase patterns to identify product and buyer clusters that are having attraction to purchase more or at risk of going to a competitor along with identifying opportunities to cross-sell or up-sell with possible changes to online content delivery to improve conversion. Here in this case, monetization is to gain an upper hand or advantage over competition by selling more of the similar or well-matched products more effectively. Here return is realized when revenues are increased, or margins get enhanced. Few organizations use patterns of system access and purchases that are mapped against external credit procured and geo data to identify the behavioral characteristics of the risk prone accounts. Based on these patterns, it is easy to identify specify fraud opportunities. This type of approach reduces loss and all its operational cost associated with it through the value chain, thereby creating an advantage over competitors and generating a return on the data assets.

Few enterprises follow Odd Man Out / Differentiator Model delivery of service or value to the customer at zero to negligible fee to customer. Here Monetization is achieved basically through building brand loyalty or developing appealing value add services that may serve as swapping barriers. Customer receives the additional or extra-ordinary service without paying for it, in expectation that the said additional services will distinguish the company and, in some way, enhance the brand or create loyalty, leading to a monetization scheme that is difficult to measure and clearly quantify.

In Premium Service Model, data is monetized through a fee based premium service to direct customers or by a subscription basis accessing a service portal. Monetization under a standard premium service model is usually fee-based online access and returns are directly linked to incremental revenue generation.

A Cartel model is often used where data in a transformed manner is delivered to third‑party entities. They would use it for their own analytics purpose or for research in their various planning activities or product/service development efforts. Most research organizations like Neilson, GWI, &  Zeotap follow this monetization model.

1. Premium Service Model

 The premium service model has broadly the following key differentiators:

• A premium fee linked for the customers to sign up.
• Benefits should resonate well or should strike physiological spark in the mind of customer so that customer becomes ready to sign up
• Retailer gets incremental revenue by generating business insights to better target the customer and for operational improvements
• Increase in Customer Loyalty, Average Basket Size, Increase in Sales

Scenario

Suzy is a customer who shops across the luxury brand “Aura”. She comes across the notification in the shopping application she uses that she can become a VIP member for just $9 monthly by signing in the portal and enjoying additional benefits. Suzy is first skeptical whether she should sign up for such a plan, so she enquires further about the additional benefits of the plan.

The plan amuses and gives her benefits to which there is no saying “NO”

• Suzy you are special to us and a privileged customer. As a VIP member you will get onetime opportunity to attend the Virtual Aura Fashion week on 24th September. You also get a free “closet consultation” with our very famous inhouse fashion expert “who can help you understand and redefine your style.
• Suzy also gets the privilege to get access to the latest fashion articles and asses to pre-market launch of all products. She gets free shipping service on her online purchases
• Suzy is one person who in the past has always purchased trending articles. Seeing the benefits of redefining style, getting to attend a major virtual fashion week event she becomes interested to sign up for a monthly program

Business Outcome

• Suzy signs-up for the VIP membership by filling all relevant data information providing “Aura” the relevant data to target the customer.
• The recommendation engine of “Aura” uses this data to recommend personalized products which resonates with her brands, interests giving an opportunity to cross-sell and up-sell
• Suzy becomes a loyal customer and tweets “Thank You Aura” on social media, becoming a brand advocate
• Even if she signs up for a monthly deal “Aura” gets access to her data and tries to get her addictive to the services provided under the VIP plan so that she becomes a yearly member

2. Differentiator Service Model

The differentiator service model has the following differentiators

• Value to the customer at zero to negligible fee. Customer is delighted to get additional benefits and hence there is an opportunity for repeat purchase
• The rewards should be valuable enough to increase purchase propensity
• Retailer gets access to basic customer data like past purchase history, email, birth date, mobile number. There is relative increase in basket size through repeat purchases and customer loyalty

Scenario

Christine is a regular customer at the Coffee Fiesta. Paying at the POS one day the store associate asks if she would like to become a member where can get loyalty points on each purchase, she makes. Through the loyalty points she can buy her some additional stuff once she reaches a desired level. Also, she gets additional Happy Hour offers and discounts. Since Christine had to pay nothing, she signs up for the plan

Business Outcome

• Christine becomes addicted to the coffee and becomes a repeat customer
• Coffee fiesta gets the customer data for more targeted promotions
• There is upsell, cross sell opportunities and an increase in sales for Coffee fiesta.

3. Return on Advantage / Competitive Advantage Model

The return on advantage model has the following differentiators

• Internal transactional data combined with third party data to bring business insights for new product development, better customer segmentation and targeting and bridging the gap between the online and the offline experience
• Customer segmentation techniques such as K means clustering and identifying purchase patterns are key to identifying opportunities to better target customers

Scenario

Lucy is an existing identified millennial customer who has been shopping at the Bandbox retailer. She has already signed into the shopping application of the retailer and also using the clickstream analytics data the predictive engine records that Lucy has searched “Avocado” 6 times in recent past 4 days. Based on sales data she appears to be a profitable customer. Using the historical analysis, we segment her in the category of loyal customer based on her sales and trips.

A predictive algorithm predicts that she will be visiting the Bandbox Store in Phoenix on third week of October. There is a new salad dressing which goes very well with avocado and since she has been searching “Avocado”  for many times may be there is a high probability she may like it .Lets sell this with 25% off as a personalized recommendation .

Business Outcome

• Customer targeting through segmentation techniques and identifying what my segments are buying, where and how?
• More analytical techniques can be designed and hence there is more data monetization that can be realized if we are able to uniquely recognize the customer
• Greater opportunity for cross and upsell and even devising localization strategies since we have demographics data.
• Geo-clustering of stores and zip codes can be done to understand demand patterns
• Major issues occur where the online and the offline data has to be combined to generate business insights

4. Cartel Model

The cartel service model has the following differentiators

• Data availability is through third party organizations like Nielsen. Performance benchmarking, reports around customer behavior and attitude, consumer survey data are some key examples of different types of data
• More organizations are now banking on such a model to use their data as an asset not just for improving their businesses but also provide access to their data for other business partners. This is called as direct monetization
• Businesses may want to sell advertising like Facebook and google selling their data to business partners for advertisements or sell direct raw data or insights to target their customers

Scenario

Due to Covid from the panel data, survey reports we see there would be an increase in the demand of products for nuts and products under “Health and Breakfast Category “. The demand is expected to increase by overall 10%. Taking into account various parameters like Covid death rate, sales panel data and clickstream data from google we are able to develop a time series model that forecasts the sales for product category “Health and Breakfast” for next 6 weeks.

Business Outcome

Data monetization is for understanding the consumer behavior data overall to analyze external demand patterns and purchase behavior for new product launch, forecasting sales etc.

Perspective of Convergence Across Industries - The Future of Data Sharing

Smart and safe data sharing will be the approach going forward. While trust and transparency is what most of customers expect but the clear advantage of making everything do smarter, safer and efficient will persuade customers to share their data .Similarly most industries will be persuaded to share their data if it means they can boost their revenue ,lower costs and can manage risks.

The consumer data has opened cross industry opportunities. Telecommunications for example generates enormous amount of data such as customer preferences, payment histories, consumption patterns, call detail records, billing and social networks, customers geo location data real time which generates a lot of insights about customer behaviour. These insights can then be used to tailor marketing campaigns for the customer by the retailers or the travel industry for instance. Similarly, data from wearable devices like Fitbit claims to have data for 6.8 million “patients”. This data can be leveraged by insurance providers to device health benefit plans and retailers to target them with the right products.

We see more and more retailers like Walmart using the customer data to elbow into new streams of business like the launching of the Walmart Insurance Services which will start selling Medicare plans. Similarly, amazon for example is disrupting by attempting to enter the pharmaceutical sector. Most of industries have understood the need for collaboration to drive innovation and are, hence joining forces. The question encircling these innovative partnerships would be around how they can encounter the challenges around data privacy, security and permissions and ownerships. Only by drawing on insights and strengths of both the partners these innovative partnerships are poised to create better results for the consumer and the society.

Conclusion

While Data driven Business, Models have become a buzz word in all the industries very few of the companies have been able to create value and have a positive impact on business revenue through use of data. There are many issues that most industries face while an organization plans to leverage existing data sources to increase profit.

First, the data comes from varied sources including complex devices in some cases (IOT sensors and cameras). Hence the data collection becomes a major challenge. Second, the monetization of data requires long term data retention and hence cost-effective storage. The storage system of such a requirement must have a tendency of scalability but do so cost effectively. Third, a lack of partnership between business and IT. For a transformation to happen it may involve the reorganization of the core business functions, change management programs aimed at organizational culture and mindset change. This type of change requires alignment and commitment from the C suite executives. Fourth, issues around data privacy and data security. While more and more companies invest in using data to improve a company’s operation, productivity, there is a second path companies are adopting which involves creating new revenue streams by making the data available to other customers and partners which involves more risks and complications .

Hence before the companies answer the fundamental question of how they can monetize their data and start looking into the technical challenges a relevant business case for investment needs to be built like any other investment plan. The obvious questions should be what is the potential revenue or value that will be created, how the value will be generated (business model), the cost of doing so and finally what the risks and complications are involved.