Marketing has come off age in last couple of decades. Before e-commerce started, marketers predominantly used outbound marketing (both for B2B and B2C) to reach out to prospects via offline and electronic (mostly TV and Radio) channels.
With the evolution of Internet, e-commerce; proliferation of Digital channels and payment services in last 10-15 years, marketers started to do both inbound & outbound channel marketing. This gave birth to Digital Marketing paradigm.
Marketers owned the budgets for paid, earned and owned media; generated leads and assisted in cross-sell and upsell.
Last decade was the decade of "Internet of People". And marketers used vanilla Closed loop digital marketing (i.e. Campaign management, Web analytics, Test and Target and Optimization) to achieve KPI's like click to conversion ratio, Revenue targets etc.
The focus was on manual analysis of customer database (mostly structured data) to create segments based on customer attributes, execute multi-channel campaigns (email, websites, mobiles, social etc), measure the effectiveness, do test on small audience (persona)
and derive insights through analytics and fine tune the campaign for better outcome. Marketers also began to optimize the marketing budget with basic attribution capabilities and do simple marketing and media mix modelling to maximize revenue.
I think, Digital Marketing has reached a stage, where we begin to see degree of maturity in Multichannel campaign management.
Happy days , RIGHT!! Not Really. Fast forward the clock into not so distant future and we see a decade of "Internet of things" where there will be explosion of mobile, sensor, wearable devices and explosion of data emitted & consumed by them. This throws
a great challenge to marketers. They need to deal with huge volume of structure and unstructured data and do marketing based on events and triggers on real time.
In my view following are the key Technology trends and high value Banking use cases, which will drive next generation Digital Marketing (i.e. data driven real time), where we will see the rise of the predictive & prescriptive analytics:-
1. Natural language processing - Most of the Technology vendors for Social & text media sentiment analysis and trend spotting use one-dimensional text analytics and NLP techniques. This has accuracy level of approx. 70% to detect emotion
and intent. It's still not able to detect important nuances like irony or sarcasm.
But, once those capabilities mature, marketers will use these for branding and sales to derive customer intends and predict segments for Targeting more accurately.
Use-case - Potential high value use-case for banks will be ability to do product ideation or Services through Social intelligence (where banks starts to mine and own the data and create the new product or service - a very different model
2. Big data & in-memory Analytics - With the increasing maturity to handle large volume of structured and unstructured data in real time through Apache Hadoop based MapReduce parallel processing framework; efficient distributed storage systems
(file system and in-memory Database) like HDFS,SAP HANA, Cassandra; real time in-memory computing like Apache Sparks, are giving enormous capabilities to drive real time prediction and advising.
Marketers can start to discover data, spot statistical correlation and apply propensity modelling to create Need (profitability) or Value (lifestyle) based segmentation. It will also allow advanced attribution forecasting algorithms and "what if?" analysis
to drive marketing & media mix optimization; It will be the beginning of meaningful predictive & prescriptive analytics marketing triggered by event & real time data.
Use-case - Potential high value use-case for banks will be the ability to use mobile wallet as a trusted payment advisor - Driving higher sales through loyalty, rewards and discounts.
3. Machine learning and behavioural science - with the evolution of psychology and other social sciences; it could become a source for business intelligence and help marketers to do marketing better. The Neuroscience is still not matured
enough to be adopted at large scale. But, marketers can start to use Machine learning algorithms to understand behaviour through engagement techniques such as gamification and refine segmentation and attribution modelling to improve real time marketing outcomes.
Use-case - Potential high value use-case for banks will be Gamifying a customer engagement scenarios to understand psyche & emotion. Once outcomes are measured feed the outcome into redesigning the customer experience with Web and call centre
4. Internet of things - We will soon be surrounded by mobile devices, wearable & sensors & intelligent display boards everywhere - Every device will emit data & Marketers need to capture them to create a 720* view of customers. With evolution
of API management, Big data, in-memory computing, NLP, Machine and cognitive learning capabilities, marketers will start to truly realize dream of
anytime, Anywhere marketing; Ability to detect an event, create recommendations and push the content in real time through devices like smart phones, Google Glass, advertising board, smart TV or watch will be achievable.
Use-case - Potential high value use-case for banks will be real time Marketing through Wearables like google glass; where Mobile Wallet and devise will act as information processer and receiver; but Glass will be the used as content delivery
platform or even the intelligent delivery display boards will do marketing on real time based over audience nearby.
Predictive analytics will give rise to Digital Marketing 2.0, a begining of
anytime, anywhere marketing paradigm.