Digital transformation: Separating the signal from the noise

The following post is published by the Co-Founder and CEO of Zinnov, a leading Globalization and Market Expansion Advisory firm.

Digital is the new default, making it imperative that enterprises undergo digital transformation to stay relevant. Before embarking on a digital journey, companies must ask these questions: Where do I start? Is my business ready for digital transformation? How do I know which digital scenario to prioritize? How can I distinguish the signal from the noise of options?

Assessing business and digital priorities

An enterprise taking the first steps toward digital transformation should start by identifying the business problems that affect its top and bottom line, and by determining how overcoming these would have a net-positive business impact.

Enterprises can analyze the array of digital trends across four parameters. First, they should assess how advancements in technology, such as machine learning and artificial intelligence, have disrupted existing business models. The second parameter involves gauging which part of the company’s value chain would be impacted the most. The third requires an understanding of the competition—both incumbent and recent, including the disruptors focused on that specific technology. The final parameter is the business impact the technology would have, which can be measured in terms of revenue enhancement (or top-line impact) and operational efficiency (or bottom-line impact).

4-Parameter Structure Approach to assess Digital Priorities

This structured approach is relevant for enterprises across all industry verticals. We put it to the test by analyzing and prioritizing real-world digital scenarios.

Modern businesses companies are looking to drive business impact across key customer targeting and engaging efforts, such as acquiring new customers, pushing customized offers and recommendations, and enhancing customer service. We examined four trends to separate the promising technology—the signal—from the din of options that have yet to prove viable.


“Personalization” has been an industry buzzword for years. Advanced technology such as beacons (which allow mobile apps to deliver content to users based on their location) and chatbots (programs that simulate conversation with customers online) have completely disrupted traditional customer targeting and engagement.

A prominent French retailer implemented beacons that welcomed shoppers, displayed coupons and suggested products based on purchase history. This led to a 400% increase in user engagement within seven months. Similarly, a major global airline enabled its passengers to get answers to simple queries using chatbots for an unprecedented quality of engagement.

In addition to enhancing customer experience and the sales and marketing aspects of the value chain, these personalization tactics also improve workforce efficiency. We have already seen an influx of 100 startups in the personalization space that have secured more than $100 million in funding. This suggests that personalization is a signal worth listening to.

Machine learning

Machine learning technology has led to a shift from a predominantly reactive model to a highly proactive one. Enterprises can not only explore potential cross-sell and upsell opportunities with existing customers, but they can also identify customers with a high probability of churn and target them with offers to enhance retention. A popular life sciences company in the US leveraged predictive analytics for a 300% increase in cross-sell and upsell and a nearly 80% reduction in customer churn. More than 150 startups in customer analytics have emerged, with $500 million in total funding to date. Our verdict: Machine learning is a strong signal.


Chatbots have been deployed by top retail stores, bank branches, hospitals, restaurants and hotels to greet customers and respond to simple queries in their local language. However, customer research studies suggest that 83% of customers prefer speaking with a real person. For this reason, a popular US retailer retired its use of chatbots shortly after piloting them in its stores. With respect to the value chain, chatbots primarily impact the store or branch experience, with minimal effects elsewhere. And chatbots do not add directly to revenue—they serve only to increase operational efficiency. This trend has shown poor performance overall, so we deem it noise. It’s wise to wait and see how chatbots develop down the road.

Speech analytics

Finally, call centers are using the advanced technology of speech and voice analytics to evaluate customer behavior in real time to gauge satisfaction levels. A German luxury automobile manufacturer implemented speech analytics to reduce long hold calls by 70% and call avoidance by almost 100%. A Fortune 100 US retail bank reduced customer churn by more than 33% using a speech analytics–based call-forwarding mechanism. In terms of startup activity, speech analytics has generated minimal interest. This trend is a medium signal—more time will show if it is worth pursuing.


As the rapid march toward digital transformation continues—and new digital scenarios emerge—enterprises must determine which will help them stay relevant in their industry. Companies that can pick the right signal to follow in advanced technology can tilt the digital balance in their favor. This will position them to deliver compelling customer experiences that drive enhanced retention and advocacy, in turn seeing an exponential impact on their businesses.