The Foolproof Case for Embedded Analytics

Overview

We are in the midst of a paradigm shift, one that is dramatically changing the way organizations value and leverage analytics. Organizations no longer ask why they should invest in analytics, but what and how they should invest in them. Otherwise, they risk falling behind.

Whether the end users are internal or external customers, embedding analytics into applications and products can help visualize insights to the point of decision, creating increased value to end users. In return, organizations benefit from top-line growth and improvement to their bottom line. 

The concept of embedding analytics is not new. Organizations have recognized this value for several decades and, to quote Wayne Eckerson, “The functionality of embedded analytics has evolved from static reports in the 1990s to interactive reports and dashboards in the 2000s to self-service, predictive, and blended analytics today.” Today’s advancements in technology have made it possible for organizations to deliver robust analytics to their customers faster than ever before. Their end users in turn have also benefited, gaining insights with more depth and breadth through an improved user experience. This shift has prompted a large and growing market within business intelligence, called embedded analytics, that has shown a much higher rate of adoption than traditional BI or analytics, according to a report by the Eckerson Group. 

Although many companies understand the importance of leveraging analytics for their customers, “surprisingly few companies know where and how analytics can create value,“ according to McKinsey & Company. After gathering insight from the C-suite, McKinsey recognized the following themes: “Organizations are moving from hoarding data to sharing it. Some are pooling data as part of industry consortia, increasing their comprehensiveness and therefore their value. Product-based organizations are adding data and analytics to their offerings as value-added services. Some have gone further, charging for the analytics-enabled service rather than directly selling the product.”  There are many options and routes an organization can take after deciding to make this a strategic priority.

Market forces

Several drivers are aiding in the accelerated adoption of embedded analytics.

Explosion of data

Organizations are amassing a prodigious amount of data, be it transactional, machine, or social data. More traditional businesses, like manufacturing, that have shifted to smart, connected devices have a new opportunity to leverage the data they already own. The sensors from the devices are emitting large amounts of data points per minute, giving them a wealth of data they can leverage in multiple ways. Some of these include monetizing the data, creating a new product or service for customers, or leveraging the data internally to improve processes and workflows.

Recommendation: Canvass your current and future data landscape to assess new opportunities by looking at:

  • What is your data worth to potential customers?
  • How differentiated is your data from competitors’ data?
  • Who exactly would find the data useful?
  • How can the data be combined with other data sources to deliver increased value?

Increased customer expectations

As customer expectations and the adoption of analytics both continue to rise, customers will come to expect or require an analytics component as part of your product or service offering. Critical business key performance indicators (KPIs) such as net promoter score, churn, and retention rate are influenced by the organization’s ability to meet customer expectations. 

Recommendation: Leverage your organization’s customer metrics (i.e., churn rate) and review to determine where you should focus and what customer needs can be met through a new offering.

Hyper competition

Now more than ever, competition has become one of the highest risk factors and priorities for any organization. A recent survey conducted by NewVantage Partners on how executives in large corporations view data found that one of the biggest concerns is the risk of disruption from new entrants. “Almost four in five respondents said they feared disruption or displacement from firms like those in the fintech sector or firms specializing in big data,” according to the survey. Some newer entrants have the advantage of specializing in analytics and big data. Unless analytics and big data have been a core principle and capability since your organization’s inception, it is difficult to pivot in that direction without having economies of scale on your side. Even then, straying from your core business by building and maintaining an in-house solution in an effort to compete is not a cost-effective nor risk-adverse option. Leveraging an analytics provider to embed analytics into your product allows product and development teams to focus and innovate within their core business, rather than exhausting internal resources to build products they are not experts in.

With the increasing demand to be competitive, businesses have turned to the advancements in analytics to capture new opportunities and markets. In a recent survey, McKinsey & Company found that “across industries, respondents see the use of data and analytics increasingly upending the competitive landscape.” Respondents were nearly 2.5 times more likely than before to report traditional competitors launching new data and analytics businesses, McKinsey reported. If it is not a priority for your organization, it is certainly a priority for one or more of your competitors, increasing the stakes. The cost of inaction is something to also consider. Historically, the first-to-market in a new product category has a first-mover advantage and is likely to capture more new business and a larger portion of market share.

Recommendation: Perform a market scan of your competitors and assess their data and analytics capabilities and overall strategy. This will give you a foundational understanding of the market opportunity and lay the groundwork for determining which capabilities to include in your minimum viable product.

Business opportunities

Before investing in embedded analytics, it is critical to make a business case for doing so.

Enriching current product

Embedding analytical capabilities is a surefire way to make a positive impact on the end users’ experience. When you informationalize your offering and embed analytics into your customers’ workflow and process, you create a stickier product that your customers will depend on for a vital part of their business, in turn increasing retention and satisfaction.

Your customers might also be data-driven organizations that see value in the data from your service offering and might want to leverage it as part of their analytics strategy. This presents the opportunity to inject your organization’s subject matter expertise and provide even more value than if your customers leverage the data from your product on their own.

This also allows organizations to charge a premium for an additional set of capabilities, vastly improving the top line. In fact, Eckerson Group reports that independent software vendors say embedded analytics increases the value of their applications by 43% and enables them to charge an average of 25% more. 

Monetizing data

Organizations are producing and essentially sitting on a wealth of data. McKinsey & Company recommends that executives “connect the data strategy to the analytics strategy.” They can start by understanding what data they have and could monetize. As Gartner put it in its 100 Data and Analytics Predictions Through 2021 report, “Information itself is being recognized as a corporate asset (albeit not yet a balance sheet asset), prompting organizations to become more disciplined about monetizing, managing, and measuring it as they do with other assets. ”This includes “spending” it like cash, selling/licensing it to others, participating in emerging data marketplaces, applying asset management principles to improve its quality and availability, and quantifying its value and risks in a variety of ways.” Adding further proof of how data monetization can fuel growth, McKinsey & Company found that high-performing organizations report a greater variety of actions to monetize their data — with greater revenue impact. This includes adding new services to existing offerings, developing entirely new business models, and joining with similar companies to create a data utility.

Build versus buy

If you are looking into building an analytics solution, there are many factors and implications to be aware of:  

  • Resources: Amount of resources required to build an analytics offering from scratch (i.e., development headcount and time, technology to support)
  • Time: Amount of time estimated to build versus optimal time to bring to market
  • Cost: Operational costs to build and maintain the solution and the opportunity cost of the development team losing focus on your core business

Often, organizations that choose to build an in-house solution have found difficulty in scaling and maintaining the solution in a cost-effective manner. They also encounter challenges in effectively developing and continuously improving a robust analytics offering that satisfies their customers and delivers substantial value.

Choosing to buy an analytics solution can be an arduous decision. However, today, it is much more common to embed an analytics solution by working with a trusted partner and embedding its product through an original equipment manufacturer agreement. Embedding analytics is proven to be the easiest and most effective way to offer analytics to your customers, allowing you to bring a quality product to market faster and with fewer internal resources. In doing so, organizations gain several business benefits:

  • Reduce total cost of ownership
  • Faster time-to-market
  • More efficient approach to security and scalability for growing data and users

Making the business case

Integrating analytics into your product or service helps you:

Making the business case

Sample business value framework

It’s also important to consider the many KPI metrics that are critical to the success of your business when analyzing the business value of embedded analytics. For example, if an organization is generating $100 million in revenue annually, let’s review an approximation of the positive impact embedded analytics can have: 

KPI MetricsResults of Embedded Analytics
Win RateIf the organization’s revenue for new deals is growing 30% a year with a 10% improved win rate due to embedded analytics, the result is an additional $3 million a year in revenue.
Customer ChurnIf customer churn is 10% annually, reducing customer churn to 5% will result in a savings of $5 million.
Revenue/UpsellsIf a product asking price is $20,000, embedded analytics can increase premium pricing by 25%. In an example of 1000 customers, that translates to an additional $5 million in revenue.
Cost ReductionIf producing manual ad hoc reports takes 2 weeks (80 hours)/month and costs $100 an hour, analytics could reduce the time to 8 hours/month, saving 72 hours of labor time/month, or $86,400 a year.

Conclusion

Whether it’s providing analytics on data that is already collected or creating a completely new product, organizations have the opportunity to delight their customers with fewer financial resources than ever before. When making the decision on whether to invest in integrating analytics into your product or service offering, it’s important to assess the business case, as well as opportunity costs.

Top takeaways

  • Explore all use cases to opportunistically leverage your data and analytics
  • Assess each business case with opportunity costs (i.e., there is a cost of inaction)
  • Ensure your data and analytics strategy is aligned with your business goals

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