Monday, 13 August 2012

Deploying Advanced Analytics on Basic Financial Statements for Increasing Marketing Effectiveness

Deploying Advanced Analytics on Basic Financial Statements for Increasing Marketing Effectiveness



Genesis:
More often than not initiatives to increase marketing ROI are greeted with a lot of inertia and skepticism. All the more so in the banking sector, where marketing is more about seeing opportunities, conceptualizing new ideas, devising eye-ball grabbing campaigns ,and at best, generating the required ‘buzz’. The all essential ‘pull factor’ that brings a customer to your bank. Beyond this the conversions are more a factor of the customer service and the product teams. However, have the banks ever paused to consider the effects of missing out on a possible ‘target’ customer for a specific campaign? Or even more harmful, bringing in an incorrect ‘target’ who goes back feeing disgruntled about the product (for which he was never the right target to begin with).

The Background:

Banks offer a more diverse portfolio of services than before, and they do so over a wider range of channels. While this trend has given banks more latitude to compete, it has made formulating and modulating marketing strategies, tactics and programs considerably more complex. That’s because as the range of options has grown—a good thing by any measure—marketing resources are as scarce as ever. To optimize how they invest them, banks need a way to continually measure their effectiveness, learn what works and adapt over time.

On a positive note, it’s great that banks are considerably investing in costly primary research and reaching out to their customers/potential customers for their attitudes, preferences and disposition towards their products and services. However, the essential question is – Are banks effectively utilizing the terabytes of existing customer data already within their custody? The immediate answer could be an overwhelming ‘Yes’ for the larger banks with all the huge investments in data warehousing, business intelligence applications and scores of analytical teams. For the not so bigger banks, the answer could an ambitious “Yes, we’re getting there”. But some of the key questions for both these segments of banks would be –

  1. Do the fast-paced marketing teams have an ongoing dialogue with such fortified warehousing and BI structures? Or do they exist as ‘happy to be mutually exclusive’ departments?
  2. Are ‘Analytics’ and ‘BI’ still perceived to be statistical warehouses for more strategic initiatives? Teams that are more about using rocket-science statistics for pricing and product development decisions.
  3. Does the term ‘Analytics’ still sounds intimidating for most marketers to reach out and deploy their marketing instincts?
  4. Does it evoke inertia and/or phobia of being too data intensive? And thus too time and effort consuming in arranging and organizing the mostly unstructured data.
  5. And along the way, has it become a widely accepted phenomenon of Analytics being too expensive for all its upfront licensing and implementations costs? 

For all the myths, perceptions and exaggerated expertise around ‘Analytics’, the simple premise for this blog piece is the basic question of (and pardon me for the repetition) ‘Are banks effectively utilizing the terabytes of existing customer data already within their custody?’ Are the banks seizing the huge opportunities offered by simple (and rich) transactional data in a customer’s monthly financial statements? A data that is ready to be consumed from the word GO! No structuring, no arranging, organizing or sanitizing. It’s there. It’s always been there. And it’s a dynamite of readily available information at no incremental cost (as in Primary research). And the biggest advantage is that it’s hardcore behavioral data that is completely insulated from the ill-effects of sampling, methodology, techniques and data collection practices of primary research. It’s all about what your customers actually are. How they have been actually behaving (over a period of time) instead of what they can do, intend to do or aspire to be like.


The Idea:

  1. Reap the benefits of advanced analytics in not-so analytics friendly (and perhaps not so prepared) environments piloting through available transactional data.
  2. Cut the lead time, inertia and phobia that’s associated with undertaking advanced analytic projects.
  3. Make analytics more accessible to the tactical and fast-paced world of intuitive marketers. Insights that reaffirm their conviction to pursue certain initiatives. At the same time, highlight hard-entrenched trends that dissuade them from committing costly marketing mistakes.
  4. Identify and exploit finer consumer niches (as evident in each customer’s actual financial transactions) rather than trying to milk homogenous mega-segments (most often force-fitted) through mass-market marketing initiatives. Not every HNI thinks and behaves the same. So how then, is he/she expected to be driven by similar stimuli?
  5. The core idea is to shift from the ‘marketing-as-an expense’ mindset to the idea that marketing is a true profit driver.  
  
The Methodology: 

  1. It starts with creating a mega data warehouse of all existing transactional data across all customers over the past 3 years…atleast. And as it goes in statistics, the more the merrier. 
  2. Go granular! Start with a detailed understanding of each customer’s banking needs drawn directly from his purchase patterns, lifestage trends and changes in purchasing power over a longer period of time.
  3. Using predictive analytics models, each customer is “scored” on their likelihood to purchase each product in a bank’s portfolio. The corollary benefit of this approach is that it helps the bank’s marketers to pinpoint product clusters that represent “sweet spots” for cross-selling opportunities.
  4. The basic principle in such an approach is a continually evolving model. The model’s analytics uncover new behavioral patterns that can be translated into new marketing programs, as well as to fine-tune existing ones.

The Benefits:

  1. The biggest benefit would be the degree to which this model can be woven into the company’s marketing ‘DNA’.  The ability to monitor each (rapidly evolving) customer and align tactical marketing decisions in sync with the most profitable opportunities.
  2. The model relies on constantly updated customer account information, enabling it to detect changes in service consumption patterns and preferences. It generates customer segment profiles on purchase patterns, spending power and lifestage and allows a bank identify and target the most attractive segments.
  3. The predictive analytics model provides the basis to shift marketing resources from lower performing programs to those with the highest ROI.

The Possibilities to Leverage this Model:

  1. Analyze customer behavior to identify key predictors of customer satisfaction.
    1. Measure and track customer loyalty in multiple ways to target retention efforts.
    2. Satisfy and retain valuable and profitable customers and attract others like them.
    3. Increase customer satisfaction and lifetime value by continually tracking customer attitudes and responding to emerging problems and opportunities.
    4. Prompt people (or systems) to proactively address issues of customer satisfaction, applying appropriate resources (e.g. rewards, promotion, loyalty program) to improve customer retention.
  2. Develop and deliver customer-driven products, services, tie-ups and alliances for target customers.
    1. Identify customer segments and use them to better target promotional campaigns.
    2. Cluster customers into groups, determine what characteristics are common and what characteristics are different between groups.
    3. Optimize marketing efforts by testing concepts, imagery and messages before deployment.
    4. Identify which customers are likely to respond to specific promotional offers.
  3. Increase the profitability of customer interactions by maximizing the value of up-sell or cross-sell opportunities.
  4. Make customer-centric decisions with far greater confidence.
    1. Gain predictive insights that improve marketing campaigns, increase satisfaction and ensure loyalty.
    2. Empower business users with the ability to create and operationalize predictive analytics.
    3. Produce more accurate predictions by utilizing all available data collected about customers.

To conclude, what can be the expected Success Criterion? Where could banks see immediate Returns and Benefits ?

Well, here are some immediate marketing victories -
  1. Increase in response to marketing campaigns across all the essential campaign parameters i.e. awareness, enquiries/walk-ins and conversions.
  2. Reduction in all below the line campaign costs i.e. direct mailers, calls or even mass-market banking activities that can be sharper and narrower in their target audience invitations.
  3. More entrenched and involved customers that can be well-reflected in parallel Primary research activities as well (higher awareness and disposition, lower possibilities to deflect, higher proportion of multi-product customers).
  4. Improvements across the board in key metrics like –
    1. Customer lifetime value
    2. Gains in YoY customer spending
    3. Reduced Attrition
So now, here’s circling back to the basic question once again ‘Are banks effectively utilizing the terabytes of existing customer data already within their custody?’ Are we ready to unravel the huge opportunities offered by the humble (and yet powerful) monthly financial statements? Can Analytics shrug off a bit of its intimidating persona and be readily accessible for the instinct-driven Marketing. Can Marketing evolve to be more Instinctively Intelligent?

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