Jun 24, 2018

Jun 22, 2018

Jun 21, 2018

Don’t segment, just micro personalize your offers

Nowadays, more and more companies are becoming aware of Next Best offer or NBO as it is commonly referred to as.
This three-lettered acronym stands as a derivative of understanding a customer better. A derivative of how streams of transactional data, newfound assets such as social media data, paid data sources and click stream information are being fed back into one gigantic repository of servers and machines. This is resulting in a classic case of mountain coming to the prophet albeit the prophecy. For example, a bank through a plethora of its statistical algorithms gauges where its customer is spending money, which is transactional data. So next time the customer spends money on the same platform, the bank after having done its homework is ready with an ad or a banner. Couple of situations to ponder over!
• What if the customer doesn’t go to the same platform again?
• He or she shops elsewhere on some other medium?
• His/her choices have changed?
Segment-based marketing’s capitalization stops here. The crux of the situation is that, the marketing tactic is segmented and not micro personalized in situations like the example given above.
So, how personalized is your communication?
Our communication with our targeted and intending customers should move from segment-based marketing to micro-personalization. Now here is the moment of truth – following a customer will soon become old school. Being where the customer is going to be will be next up and I believe it will be so for a long time to come. We need to map a customer’s identity with a 720 degree view via their social handles, we need to keenly listen to their Social Media feeds and these feeds will be in turn fed back as customer notes into the algorithms (for our marketing programs).
Firstly, we need to understand our customer’s digital footprints by measuring their habits. In the above example of the bank, following micro-personalization, it can be in a position to understand why is a particular channel working and others aren’t. Connect all the touch points of the customer to have a single-view.
Secondly, these plot points can be extrapolated on a graph of lifetime event series. So, a bank can predict the next lifetime event of a particular customer and be there when he or she engages next. Furthermore, these data structures can bring seasonal impact for life changing events, too.
Thirdly, building hyper personalized customer persona. By understanding recent transactions of the customer, create his or her persona. Understand his/her needs and moments of truth. Analyze whether customer accepted or rejected the offer? All these data points should be fed into machine learning algorithms to make them act smarter.
NBO marketing is a customer-centric approach. The solution is not in finding the customer and keeping at it. It is in the measurable magnitude of the centricity part. Our entire decision taking management philosophies should be centered around analytics to provide real-time offers with hyper-personalization. These are the proverbial needles and the rest is just haystack. Or that’s where the companies today are evolving.
Next Best Offer marketing impacts business drivers. In a cyclical loop they will impact in increasing offer acceptance rates, better up-sell and cross-sell. These numbers are going to reflect back in operating margins, increase in campaign response rates leading ultimately to customer satisfaction and loyalty. This can be achieved only when all the data sources listed above are integrated and proportioned with what the customer wants and what banks want to sell.
NBO might not be a be-all do-all parameter. In principle with proven metrics it could be the silver lining of sending the right offer in the right channel with the right message at the right time to the right customer.

Machine learning models, alternative data sources expand banks’ credit-scoreable population

With machine learning (ML) models, lenders can now directly implement algorithms that can assess customer risk and assign scores to customers with little or no credit history.

The recent implementation of the General Data Protection Regulation (GDPR) within the European Union and European Economic Area (EEA) gives consumers control over their personal data and aims to simplify the international business regulatory environment. While the GDPR’s ultimate impact on the global financial services industry will evolve, one thing is clear today – leading-edge data management systems are more critical than ever. For lenders, the ability to efficiently score and approve the most credit-worthy customers while maintaining regulatory compliance is a must-have competence.

Credit card debt in the United States reached its highest point ever last year, surpassing $US one trillion, with the average household carrying US$16,883.[1] So it goes without saying that technology that could improve a bank’s returns on credit held, or that could grow market share, are worth a second look. It’s no surprise, then, that both incumbent banks and startups are exploring innovative new underwriting models.

Are you overlooking deserving customers?

Historically, consumer credit tests have included an evaluation of an individual’s creditworthiness, debt burden, borrowing frequency, and social and community considerations. However, the linear nature of these statistical models makes it difficult to include and analyze the growing volume and variety of Big Data that can help lenders make informed credit decisions. A machine learning model, unconstrained by some of the assumptions of classic statistical models, can yield insights that a human analyst might not reach.

Through machine learning (ML) models, lenders can now directly implement algorithms that assess customer risk and assign scores, even to thin-file or no-file customers (individuals without recent credit files or those with little credit history).

Thin files may be thick with opportunity
Considering that one in 10 adults in the United States has no credit history with one of the three leading credit bureaus, algorithmic ML capabilities could have an enormous impact on lenders’ revenue-generating potential.[2]

Moreover, ML results are explainable for compliance and internal and external communication. Machine learning models can liberate banks from exclusive reliance on third-party credit companies.

In fact, ML models enhance credit bureau reports by recalculating existing consumer credit indexes based on external data sets,[3] which often enable banks to more meaningfully assess and accept previously overlooked credit applications.

ML models can interpret countless consumer attributes.

Whether a bank wants to more efficiently manage current credit customers or take a closer look at the millions of consumers currently considered unscorable, alternative data sources can provide a 360-view that is far superior to traditional credit scoring. Third-party data sets can unveil consumer information (such as social media activity, texting, travel history, frugal phone patterns, or on-time utility bill payments) that can increase the predictive accuracy of the credit scores of millions of credit prospects – consumers who may be desirable but have been invisible to lenders before now.

ML models that leverage alternative data sets can target population segments ignored by banks that rely exclusively on traditional credit-scoring models – which can lead to a commanding competitive advantage.

A strategic approach, knowledge of the dynamic regulatory landscape, and access to large amounts of data are must-haves for banks aiming to establish an ML-based credit index to expand their customer roster.

From data ingestion and preparation to discovery and real-time data analysis that uses open source or commercial tools, Capgemini’s Analytics and Data Science team – a part of the Insights and Data practice – can help banks learn more about best practices to reach their business objectives.

[1] CNBC, “Credit card debt hits a record high. It’s time to make a payoff plan,” Jessica Dickler, January 23, 2018, CNBC.com https://www.cnbc.com/2018/01/23/credit-card-debt-hits-record-high.html
[2] Consumer Financial Protection Bureau, “Who are the Credit Invisible?” Michele Scarbrough, December 12, 2016, https://www.consumerfinance.gov/about-us/blog/who-are-credit-invisible/

[3] External data sets might include: business and firmographic information, personal contact and directory assistance information, business news coverage, online and offline data indexing, social media sites, corporate sites, SEC filings, blogs, government data sources.

Aug 18, 2015

Facebook Groups Analytics tool

Analyzing the Facebook group is really a challenge. I recently came across a wonderful tool grytics.com . I tried using that in one of my group and insights are really great !

Some of the screenshots of this tool are given below:

How grytics.com calculates influencers:

Influencers are group members whose posts are the most liked and commented by others.
The Influence ratio is based on the number of likes + 2*number of comments.


Here you will find the definitions of the various statistics Grytics is providing to analyse your social media groups.

Would you like to try this tool?

Jul 24, 2015

Python Tutorial : If Else condition

Here is the syntax for if else condition in Python:

pencils = 80
students = 20
if distribution>=4 :   print("OK")
else:    print("NOT OK")

This code checks whether every student get minimum 4 pencils or not. If yes, then print "OK" or print "NOT OK".