Enterprises, small or large, are always in a phase of change. Peek into the program management office of any large (annual revenues in excess of a billion dollars) telco or bank, and you will easily find at least a dozen transformation projects tagged under Digital / Customer Centricity / Crypto Assets / Data Consolidation / Metaverse. Also incumbent is a huge army of highly skilled engineers, architects and domain experts from the marquee list of ISVs, consulting firms and system integrators, all marching committedly toward the zenith of customer centricity, as everyone already knows that is the only way for businesses to survive, let alone flourish.
Yet research says those transformation efforts fail most of the time. Why? Perhaps there are many reasons: strategic mistakes with goal setting, tactical flaws in planning, operational errors during execution. In this article, I shall narrow down these issues, those that are largely in our control, and suggest a few ways to overcome them.
1. Don’t let FOMO be the driver.
Put your horse before the cart. A competing bank’s marketing campaign focused on NFT-backed digital artifacts for loyalty exerts a lot of pressure on all peers. But before onboarding a vendor to provide the backbone for crypto assets, the marketing team should list a handful of concrete use cases and assess their relevance/appeal to the bank’s customer base.
For instance, create a concrete use case like this:
“My Telco X shall mint 1,000 celebrity-signed SIMs accompanied by a collectible, say a coffee mug or watch, and distribute it to our most loyal customers. The SIM and the collectible are digitally coupled and tokenized using NFTs, making this a limited-count asset now. Customers (may) take pride in owning it. We may incite demand as the asset is limited. We may host an exclusive “Celebrity Collectible Owners Club” with the access key being the NFT itself.”
And if it sounds logical, then onboard a partner to realize the vision.
2. Remove self-imposed constraints.
Most enterprises lock themselves in with only one vendor for a specific function and let them handle the entire customer base. For instance, one vendor for CCCM, one for RTIM, one for digital analytics, etc. And each one of them caters to the whole base of 100 million customers.
Enterprises seek advanced capabilities like A/B testing from those products but do not themselves practice A/B testing with multiple vendors, where, say, two competitor products are pitted against half the customer base.
In today’s contemporary tech landscape, with most vendors taking the SaaS route, operational considerations of yesteryears should not be held as blockers for having multiple incumbent competing products.
3. Diffuse the tension between IT and business.
A common trait I find in market leaders is the presence of absolute synergy between IT and business. Meanwhile, I notice the opposite in laggards. But both are wrong and need a realignment in order to put the end customers ahead of their own priorities.
Having IT and business aligned on the right “customer” axis is pivotal to ensuring smooth and successful outcomes.
4. Don’t go for blind AI; seek explanations.
It is sad but true that even large enterprises fall prey to machine learning’s (ML) glamour and onboard many AI-heavy projects, allowing their systems to make a lot of decisions without sufficiently understanding the rationale behind them.
In their race against time, enterprises have a tendency to choose those vendors that ship with a lot of pre-built models, and knowing the trend, vendors have also inflated their stock model count. This is a potentially dangerous practice, one that can uproot a brand’s stated emphasis on customer centricity. Enterprises need to make sure they choose vendors that can provide a rationale, in business terms, for the recommendations and actions they undertake, and not go by arbitrary numbers emitted by mathematical models based on never-understood matrix transformations.
For example, if Alice was recommended a four-year mortgage loan while the CSR opines that a three-year unsecured loan is a better option, the CSR should be able to ask the underlying ML as to why it deemed mortgage to be a better option for Alice. In response, the ML should be able to give reasons in business terms (e.g., “Analysis of Alice’s cohorts reveals that there is a 3x increase in chances of bad debt when they consider a loan within six months of engaging with the bank” and not an apparently useless metric (e.g., “Alice’s proximity score to four of the deduced clusters is 0.3, 0.4, 0.1 and 0.2 with a noise level is 0.86—that’s my recommendation”).
5. Be rational and also understand the data limitations.
I have sat through business workshops on customer centricity where the need for brands to connect with the “whys” behind customer engagement is well understood, and then, we come up with a purpose for customer interaction like “To open a fixed deposit for 12 months.” There is an apparent disconnect here.
An end customer like Bob will possibly engage with an intention of “Putting idle money to better use” and possibly wants a safe bet. Hence, his preference for deposit over equity. Thinking from Bob’s perspective, fixed deposit is just a means, not his objective, and until you understand this subtle difference and align accordingly, you will never be able to transform your company into a truly customer-centric brand.
While there is no prescription for success, as Otto von Bismarck rightly said, the wise man learns from the mistakes of others. Not rushing into the same pitfalls others have just managed to come out of is important to stay on the right track.
Want to know how Pelatro can help you improve your customer engagement? Get in touch at hello@pelatro.com
Author
Chief Architect at Pelatro. Proud to help 40+ Telcos/BFSIs offer the finest contextual marketing experience to their 1B+ subscribers. Read Pramod Konandur Prabhakar’s full executive profile here.
This article was originally published here.
