THE ARGUMENT IN BRIEF
A personalised message is not necessarily a useful customer experience. Drawing on an example from his own inbox, Anil questions whether data collection targets encourage relationships or just more messages.
This past Saturday, I received what was can be labelled as a "Personalized message" on WhatsApp from Pantaloons

But can we truly classify this as personalization?
Will it leave me, as a customer, delighted?
Does Pantaloons genuinely understand me as a customer when reaching out via this WhatsApp message?
Reflecting on my experience, it's possible we would have made a purchase at Pantaloons, perhaps last year. During checkout, I provided my mobile number—entering the realm of First-Party Data.
While Pantaloons deserves credit for capturing "First Party Data" (whether consented or not, I can't recall), crucial gaps remain. The gender field, for instance, remains unfilled, paving the way for further irrelevant communications like this. It has made further errors in assuming many things here while sending the message for ‘’workdrobe’’ (meant only for working women). Do they know whether my spouse is working or not ? Even if it was meant for her but sent on my number, the assumption was misplaced.
While capturing first-party data is essential for personalized marketing efforts, it's equally important to ensure accuracy and respect for privacy preferences. Assuming gender or other personal details without consent can lead to irrelevant and potentially off-putting communications.

This scenario is not isolated; similar instances likely abound across brands today.
Where do the gaps lie?
What steps should Pantaloons take?
How can they complete the "Gender" field in their database?
Will mere completion of that field suffice to send me a message that truly delights?
What efforts are being made to enhance the information associated with that number?
This illustrates a common pitfall: Brands stuck in a so-called personalization exercise, mistakenly believing that collecting First-Party Data alone marks the endgame. It's a rushed pursuit lacking a holistic strategy.
Should there be KPIs for First-Party Data collection? And who should own them?
Should KPIs revolve solely around the quantity of PII/1PD collected?
Shouldn't they be outcome-based? But what constitutes these "outcomes"?
Should it be merely the efficiency percentage on activating First-Party Data?
What about measuring customer satisfaction levels post such activations?
How are you measuring customer experience at all touchpoints? In this case ‘’ WhatsApp’’.
Where's the feedback loop?

The fear of potential damage from irrelevant messages outweighs the allure of achieving PII data collection KPIs. Yet, some brands remain oblivious, overlooking factors like "irrelevant personalization" and "Trust erosion."
In the aforementioned case:
My PII data collection likely satisfied someone's KPI at Pantaloons (Number of First Party Data). But whose?
Sending WhatsApp messages like this may have satisfied someone else's KPI (Volume of "Personalized Messages sent"). Who at Pantaloons ensured coherence between these activities and their alignment with the company's and customer's needs?
Is there any measure indicating whether such supposed personalization activities bolstered customer trust in the brand?
Where's the measurement for such campaigns? How do we track message efficacy when no customer action follows?
Who bears responsibility for ensuring the collected data meets consent standards? Wasn't there a need for a consent audit for data collected over a year ago? Whose KPI is/was that—Legal, IT, or Marketing?
Where's the First-Party Data Strategy or, more aptly put, Customer Outreach Strategy amidst all this?
I believe there's ample room for improvement for redefining the game.
Time to reconsider First-Party data collection target as a KPI and make it more robust.
#firstpartydata #personalisation #trust #PII #DPDP #Firspartdata #consent #dataprivacy
*Disclaimer: This post is for informational purposes only and does not endorse or disapprove of any specific tools, platforms, or technologies. The views and opinions expressed in this article are those of the author and do not reflect the official policy or position of the company he is employed in.
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Original text from Anil Pandit’s article export. Claims and references reflect the time of writing.
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