Archive for the ‘Retail Analtyics’ Category

The Broken World of Marketing Data Sciences

dilbert

Do you know why the world of marketing has a response rate of 2-3% and is happy with it? Even lesser? It is because the world of marketing data and marketing data sciences is broken. And marketers don’t know a way around it.

Here’s what the data looks like typically-

  • Market Research & Panel data – For decades, marketers have been happy dealing with panel data that fulfills what they believe in. Like the huge TG for their product and innovation. So much so, that it takes years of business disasters in a row for a Nokia to understand how things have changed. But Panel Data is at best a coarse approximation. The sampling is flawed 9 out of 10 times. Flawed. I am not even talking about biases. Or, fraudulent data entry.
  • Focus Groups – Less said the better. The FGDs are not representative (by design), the insights are more often than not an accident, and there is a serious dearth of good marketers capable of running half-decent and probing focus groups. What you can usually depend on here, though, is something which is unanimously panned or applauded. Like the Kala Bandar (Black Monkey) of Delhi 6 (the movie).
  • Test & Control Groups – Several practitioners have lost their voices screaming about the need for quality T&CG setup. The only real deal here, to some extent, was the world of database marketing. Unfortunately, DBMs believed a little too much in their potential, and focused less on their improvement areas, a problem that often accompanies success.
  • POS/Warehouse Data – Logged in/ loyalty customers is what we are capable of tracking, they’d tell you. And can they track you across your properties? Their properties? Households? Don’t they have the data already? And what about the other data elements? How good are they? What about that graph model thing?
  • Big Data – Lets mine social media, twitter sentiments, facebook posts, and let us use key opinion leaders and influencers, and viral content. And we will have machine learning for real time recommendations and offers. And… I know. I believe in the possibilities. But do you really understand it? Where does it even begin?

What this has, sequentially, led to, is –

  • Lack of trust in the data – an obvious side effect, won’t you think?
  • Rise of Witch Doctors –People with creative halos, learned expressions, snob behavior, and/or a “heightened sense” of what the consumers want. The primary reason why you’d trust them – because they tell you so. And, because you have no alternate version.
  • A Kodak Moment – All traditional data providers are getting destroyed by Google, Facebook, Inmobi and the likes. These companies have brought “attribution” and measurement to the center of the discussion.

There has been, therefore, a dearth of high quality and effective use of marketing data. Take for instance, Amazon. It’s a data-science influenced company. Yet, what you get to see on the landing page tells you how broken the listings/ recommendation algorithms are. And they’ve been playing with it for a while. Flipkart fares much worse. The others are not even talking about it or worth talking about. Offline retailers have little good data. Brand managers work with incorrect data most of the time (or trade data). Yet, conversations about analytics and big data makes for good headlines.

Nevertheless, there are few philosophical departures, none too easy, to keep pressing forward.

  • Invest in data – Most companies need a multichannel data play, common denominators across platforms, and investments in “data collection”. It’s long, tough and they don’t have the right focus or resources to do it.
  • Improve data quality – Data needs to become a constant companion in every discussion. Across HR, Finance, Legal, Admin, Marketing, Sales, Strategy. Any investment in that direction would, today, mean creating hundreds of cross-walks, resolving inconsistencies, requiring layers of data cleansing, and finally, standardizing the definitions. It would be, for most companies, a journey of at least 12 months if they are smart, ambitious and ruthless. Ruthlessness is the key.
  • Data in the front office – One recurring funny moment in every consultants’ and analytics professionals’ life is walking into a cross-functional meeting, with at least 40% of the attendees reacting at some point– “I’ve never seen this data before” OR “this data is wrong, because I checked last week and it was something else”. That’s because data and analytics sits in the back-office, and reacts to distributed demands and definitions. If it were in the front office, like revenue and profits, it would be reported correctly 8 out of 10 times.
  • CEO’s Mandate – All professionals, in time, will have basic understanding of data, like they (should) have of English (as a proxy for language of the business) and Mathematics. Till then, however, it needs to be driven like a CEO’s mandate. Will that happen?
  • Data Monetization – All of this pain becomes easier if start monetizing the data itself. Data and Analytics are a cost center. They can be a P&L. Especially, if companies stop being scared of how priceless their data is. It is not. The only thing that makes your data priceless is how it is used. Governance around the usage of data might help companies more than sitting on it and hatching it.

You know one thing that would really help? Knowing when you need help and being candid about it! And – I’ll not get into the psychological reasons (I know better than data), infrastructural reason (our technology and analytics team is not ready for it) or business reasons (it is not a priority at this point) why marketers don’t ask for help.

<First published on linkedin>

 

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Daily Dose of Analytics : Coffee Shops And The Personal Touch

I have always found coffee shops to be a shared yet extremely personal space. Swarming with people, but you always get your quiet space, or the space to discuss the biggest and the most profound of topics.

Much before I started working, as a student with an insignificant pocket money, a friend and I would save just about enough money in a week or a month to the go have a cup of cappuccino at the newly opened Barista at Vasant Place market in Delhi. It was an aspirational act for us. Back then, a cup of coffee costing 30 bucks was a luxury that middle class students like me could not afford every day. I survived a week on bus passes and about 100 bucks. With chole bhature in college canteen costing 5 bucks, it wasn’t too difficult, in case you are wondering. However, But for the coffee shop manager, I was somewhat of a regular.

During my MBA days, I welcomed the opening of the Café Coffee Day inside the campus. While the poor guys had stiff competition from the legacy Nescafe machine serving super sweet desi coffee for 5 bucks or so, there were loyalists who would go to the café regularly. I would do that sporadically (continuing financial constraints). Yet, while at the café, it was a personal experience. Reason – the fellow at the counter knew me well enough by my third visit, and my order as well.

The phenomenon continued with me and Tushar playing “Jaadu Hai Nasha Hai” on the jukebox of the CCD at Ispahani Center in Chennai, or the string of coffees (mostly with biwi, TG, Shumeet, Shilpa, NehaG, Sulabh, Aziz and/or several others at Inductis) at the CCD at Solitaire Plaza on MG road. At these places, the old age touch of the coffee shop team knowing you, smiling, understanding what you’re going to order, and gradually establishing a personal connect with you was a part of the reason why I would go to the same coffee shop over and over again, even as the very cup of coffee became a standard output from one outlet to another. And more outlets, maybe closer to where I was, popped up at regular intervals. Ajay (at CCD Solitaire) even invited us for his marriage, even though he was really confused about who’s dating who for a very long time, given the NC2 combinations of coffee-ing!

Over the last few months, there are two coffee shops that I have frequented with great regularity. The Di Bella at BKC, and Gloria Jeans at Powai. However, these two are regulars because they are convenient. Whenever I am in BKC (was the norm when I was still working and continues even now with the people I meet there), it’s the only half decent option. CCD’s coffee quality has become despairingly bad in the last year and a half. GJC in Powai is also close to home, half decent coffee, has power connector points for me to work uninterrupted for some time, and enough quick bite options close by. And is open till about 1AM.

Now, in both these cases, I don’t think the folks would recognize me from one visit to another. I would probably need to strip and dance before they’d start recognizing me on my subsequent visits. Like the coffee, the customer is becoming a standardized product, is it?

The answer is no, in all likelihood. And that’s where Retail/POS Analytics should help do the job that the friendly neighborhood stores were doing so effortlessly. All the nearby stores would know me and my parents, back in the days, because of several factors – smaller/closer communities, repeat visits, continuity of the people who managed the same store over years, and lastly, a general culture of taking interest (which the modern world can called nosy as well). Retail Analytics can make this very easy for most.

An example that comes to mind – the small touches that Amex customer care often adds. For instance, last year, I called them for a query in February and they knew that my birthday and my anniversary are around the corner. How? B’day is easy. But the year before, I had some purchases around those dates, the address on my file and my wife’s file are the same, and lastly, my wife had purchases around the same date. Someone inferred it to be an important date. Not exactly what, but most likely, the flag of an important went up. The customer care executive promptly asked me if I had any vacation plans and if I needed any help. To the extent of suggesting that I could redeem some points against some of the travel options because I had a very healthy point balance.

Earlier, a lot of these required manual effort. Like that branch manager at your bank, or the store owner at the nearby store, or Ajay continuing at the same CCD for two years on the go. Now, data quality (better organized and cleaned data being made available in large volumes), and simple analysis can make it very easy for the POS person. If nothing else, a simple name to call out for and the last four digits of the credit card being swiped could start establishing the relationships, right? Next swipe, bring out that 10% discount coupon for registering – bingo – name and address collected. Follow it up with Coffee Clubs/ Loyalty – wonderful retention. Especially, for GJC, in an area where there are approx 5 or 6 coffee shops close to each other. Will occasional errors happen? Yes. But as long as the touchpoint is consciously used as a positive reinforcement, the impact cannot be negative. Analytics should take care of the machine so that the touchpoint can continue to be more human.

On that note, why hasn’t any coffee chain thought about organizing coffee evenings for groups of friends? Movies, Coffee and Sandwiches. ☺

 

[The Daily Dose series could evolve into a series of stray thoughts on analytics in daily life)

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