I often have been wondering off-late how does one define success for data-driven marketing programs - the reason being tons of hours are spent in understanding data, organizing data, cleansing the data make it ready for analysis and then time taken for building insights by analysts, getting organization-wide buy-in into the insights and developing a roll-out plan, measuring them etc.

The opportunity I see with all this, is a need to clearly define how is a company benefitting with all of this is increasingly becoming critical. No amount of insights & discovery is worth its weight in gold till it becomes actionable and value is returned in dollars for them. Also, another important question is how do such programs get more investments to provide more value year after year? I also don't see this being driven from one corner or department while participation from senior management teams and other departments is critical for its success, adoption and growing investments.

Interestingly as I got thinking about it, I got something from my archives which had an interesting reference to how to define success by Pat LaPointe.

He has got a lovely definition of defining success. Here's the forumula according to him which may be worth looking at:

It is a simple summation of all experiences of transforming insights into action and the value it created divided by the number of resources consumed to get this value and this is raised to the power of perception!

From my experience, I agree with him that the hard part is transforming the insights into action in any company. Also, perception of quality plays a critical role as enterprise-wide buy-in is critical for such programs as they need collaboration from other departments like sales, customer service, IT, channels etc. So, continued success of such programs must be exponentially raised to the perception and alignment of metrics & hence the value each of them get from such initiatives.

Would love to hear any other method of measuring sucess of data-driven marketing programs.

The year 2010 is coming to an end and the 2011 marketing plans are being drawn out by CMOs.I have often wondered how are budgets allocated year after year by CMOs and marketing managers. In fact, most CMOs use archiac methods of planning marketing spends? 

1. How much did I spend last year?
2. How much is the category or competitive brands spending during the same period?

Is there a better  method to plan and allocate marketing spends? I am sure time has come for us to look at this kind of an analytical framework.

I came across an interesting article on how this can be done and I found it very interesting. Here are some of the higlights of the approach:

Step #1: The first question the author says is for the CMO to determine or test their spend leverage.

Step # 2: Most often, the author comments, marketing departments are not in alignment with the overal organizational strategy - on margin improvement, new geography penetration, production innovation or redesign, alternative channel testing etc.. It sounded very familiar to me and it is an important item which CMOs & marketing teams need to revalidate as they build their marketing spend plans.

Step #3: To determine which marketing spends to "defund" based on impact to business over the last year.

Step #4: Then basis the above 3 steps build a strategic prioritization plan.Strategic prioritization means CMOs need to decide which geographies, which segments, which innovations, which programs need more or less monies.

Step #5:  The other interesting point made is that marketers should also look at risk issues around the environment as normally only GDP growth rates are looked at. The issues include terrosim issues, weather etc. which do pose a risk to marketing planning and spending.

I believe this is a great framework for a start to look at defining and planning marketing spends in a new year.

I often see a lot of digital marketing specialists treat social media data almost like traffic lights- a kind of sentiment monitor - Red, green and amber - here are the positive scores, negative scores etc. I wonder what marketing actionability does such visualization and presentation give to marketers. For the first few months, it may sound interesting to marketers, after that am 100% sure, they will get bored and start questioning the application and business impact of such data.

On the other hand, I see traditional direct marketers or database marketers or analytical professionals lost when it comes to social media. It is almost like finding their way in a maze and they often treat this as technology or a statistical problem that has no application in this medium. Also, they  have very little clue about this medium. There is  huge opportunity to apply  both powerful direct marketing principles, database marketing and analytical techniques to this social media data. In fact, digital marketing specialists too need learn from these  principles & techniques.

In fact here my view of how social media data is changing  all of this.

1. Direct marketing was always restricted because of lack of customer address, behavioural data and information - social media data changes all of that.
2. No more physical address is important. An address on the web, posts on social media sites can help identify the customer and their current behaviour, attitudes and concerns. Treat all of this as 'information for action'  rather than ' positive words or negative words of sentiment'. In fact sentiment on the web is dynamic. Action must be swift and dynamic to sentiments as they have a potential to make real-time impact.
3. Treat comments & posts data as response which can help build individual relationships and communities. This could have never been possible without social media.
4. Relationships in the past were based on only transactional data, today social media enables relationships based on individual behaviour - positive and negative emotions which can be deciphered. They need to managed as emotion-led customized campaigns with clear relationship metrics.

It was interesting see an article  by Bob Maclnnis & Sari Tamilio who also urges us to think similarly about social media data.

Do you agree? Would love to see your comments and thoughts on the same.

With  zillions of data points becoming available in organizations today, there has been a lot of buzz around the following:

1. Using analytics in marketing campaigns driving relevance, immediacy, reducing wastage and optimizing spends
2. Making marketing more accountable using the power of data that's becoming available like never before
3. Getting analytics closer to business - Making it simpler and business manager friendly for action to be taken
4. Getting organizations more customer-centric as customers leave a lot of trail about their behaviour - likes and dislikes

I have been always of the belief that analytics is not worth its weight in gold, if you don't make it actionable. The key to this has always been taking this power to the ultimate decision maker where ever he or she is in the organization - it could be the tele-sales rep on the phone, the customer service executive on the field, the marketing manager at customer's office etc. To make that happen, this has to be linked with performance management of employees who ultimately play a role in making this happen on the ground. Peppers & Rogers too seem to make this point in their recent  Expert Opinion( Mark Selcow)article: 

Organizations are beginning to recognize that these massive volumes of data are valuable for more than the immediate insights they reveal: By leveraging metrics, dashboards, and other PM components, data can also be harnessed to change behavior and processes.

The connection between the growing interest in analytics and PM is not coincidental. Analytics is about mining data for patterns and insights – making big discoveries that help advance big ideas. PM is about turning operational data into metrics that show performance versus a goal on a task or process that matters. Simply put: Analysis, Action.

Here are a few quick thoughts on how analytics and PM can fit together in a single project initiative:

1. Select the best projects to execute against that will help advance a critical goal, such as customer churn reduction (analytics)
2. Determine what the metrics and goals should be in each project (analytics)
3. Uncover patterns that can be addressed with process reengineering or team/individual intervention (both analytics and PM)
4. Plan out the financial impacts of making operational change, such as modifying compensation, or tightening/relaxing SLAs (PM)
5. Operationalize the insights and plans by introducing to the organization, for example, in dashboards or in incentive systems (PM)
6. Measure and control performance (PM)

This will continue to be an area of growing interest in organizations as the results then get tangible, metrics then become a measure for evaluation of employees who are responsible for making this happen.