marginally subversive

Setting up a knowledge/analytics team: Some tips

In my experience of setting up and of being a part of internal and client-based analytics and knowledge-teams, I have learned that the structure of the team is critical not in defining the roles and responsibilities.  It is also critical in the hiring process and in optimizing the search for insights.

There are three types of expertise that are needed to create a successful analytics team:

1. The Data Technicians, who gather, manage, and 'qualify'/'purify' the data one is getting - whether internally or externally

2. The Data Analysts, who take the information from the Data Technicians and analyze them using mathematics, statistics, econometrics and other techniques in response to questions and hypotheses

3. The Knowledge Integrators, who take the analyses, integrate them with other sources of information (such as consumer feedback, market evolution, business and brand lifecycle) and make them relevant to questions raised

All three are equally important and play a critical set of interrelated roles and responsibilites in the search for evidence-based insights.

One could be tempted to hire people who can do all three - but in my experience, it is difficult.  

1. Finding potential candidates who can do all three roles is one difficult affair even with the help of experienced headhunters.  They are a rare breed.

2. If one did manage to hire one who could do all three, I believe that one or two of the responsibilities could be sacrificed.  And that is a great risk.

Setting up an effective analytics team needs great thought and foresight.

the web versus the tube in China: guess who's winning...

Yes.  

You read it right:

1. Close to 60% of Chinese Netizens spend 3hours on the Web each day

2. That is three-times - THREE TIMES - than the number of Netizens who spend the same amount of time on TV each day.

And here one more...

 

So... the Rural/Urban Divide will cease to exist in the China Websphere (if it hasn't already).

 

 

(I picked these data up from Eskimon's blog.  Thanks!)

Digital, Mobile, and Social Media in China (April 2011)

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Thought for the Day

"In war a dull success is always better than a brilliant one."

- Paul Hoffman, "The Left Hand of God"

*****

These days, marketing and brand wars are no longer won through strategies learned from the past. The battleground has shifted - it has changed. The rules of engagement of eras gone no longer hold. And they continue to be redefined each day by forces that are interrelated and are multiplicative - social, technological, environmental, economic, political, the power of the individual.

being MAD...

even if you work in advertising... 

 

(Inspired by Seth Godin's article on Acumen.)

of chickens and eggs: is there a definitive answer?

I had a very interesting conversation with a good friend a couple of weeks ago about causation and inferring causal relationships haphazardly.

One of the questions that stuck in my head was this:

Are big companies big because they advertise a lot?  
Or do they advertise a lot because they are big?

Sounds like a chicken or the egg situation: which came first, the chicken or the egg?

(My friend thinks it's neither - it's the archaeopteryx.  To which I argued, "That was a bird." To which she said, "Nope, that's the ancestors of birds." ... I have weird friends, I know.)

But it is something worth pondering - because at the root of this question is the question of causality.

Causality is supposed to be simple:  

If A happens, B happens.  
Without A, B does not happen.
Therefore, A causes B.

Or is it?

What if there is a lurking variable C that is somehow related to both A and B, such that:

If A happens, C happens.
If C happens, B happens.
If A does not happen, C does not happen.
If C does not happen, then B does not happen. 

Can we still consider arrive the same conclusion that "A causes B"?

 

Let's make it even more complicated:

If A and C happen, B happens.
If A happens, B does not happen.
If C happens, B does not happen.
If A and C don't happen, B does not happen.
Therefore A and C happening cause B happening.

 But what if A and C are themselves related somehow?  Worse, what if A causes C?  Or what if there was another variable D that we have not considered that is making the presence-absence of A and of C?  Can we still arrive at the same conclusion?

I am no logician.  And I am pretty sure that there is an answer to these.  

I can already hear my psych professor going "Do a proper experiment in a laboratory setting eliminating all potential confounding variables, and measure properly - and do it several times - and get others to do it several times too".  But sometimes, experiments are not easy to do.

Even the most stringent of experiments still tend to have some flaw in it.

So what now?

All I can say, for now, is - 

And for now, I have no answer (because my brains are swimming in some melatonergic medicine).

 

 

 

 

customer lifetime value and media investment lifetime value

From good, old Wikipedia:

In marketing, customer lifetime value (CLV), lifetime customer value (LCV), or lifetime value (LTV) is the net present value of the cash flows attributed to the relationship with a customer.

The use of customer lifetime value as a marketing metric tends to place greater emphasis on customer service and long-term customer satisfaction, rather than on maximizing short-term sales.

The formula for calculating CLV is:

... where GC is the yearly gross contribution per customer, M are the associated costs in retaining customers, n is the number of years, r is the yearly retention rate, and d is the yearly discount rate.

 

Two questions:

1. In this social age, where connections between customers are far stronger because of social-media enablers/technology, is this formula still valid?

Certainly, the "gross contribution" of a customer should take into account not just her own purchases - but also the impact that her purchases have on her experience and her propensity to recommend the same brand (or not) to others.  

Additionally, the M - the cost associated of retaining a customer - should take into account the cost of encouraging her to talk about your brand and recommend it to others.  From this vantage point then, M is more than just the amount that one spends on her directly (i.e., 1to1 channels), but also things like advertising (spillover/tangential or directed to her) and effects of other people on her purchase decisions.  Amongst other things.  

How do we account for that in GC and in M?

 

2. Why is it that we don't have a similar measure for media investments?

 In media planning, we talk of GRPs, shares of voice, reach, frequency, impressions... and the cost of each (e.g., Cost per GRP, Cost per Reach Point, Cost per Impression).  Some more sophisticated companies will measure returns on investments per medium or per level of investment in a certain medium.

But these metrics - cost-efficiencies and ROI - are all essentially a measurement in a point in time - NOW.  And much as we should be planning for the issues NOW - the impact of what we do NOW will last way into the FUTURE.

So should we not also be talking about the NPV and FV of media investments whilst we are allocating moneys across different media, different levels, different markets?

 

One last question...

Why are media companies not talking about these things when they are supposed to be the investment experts?

 

 

(*** Written whilst multitasking and in a hurry.)