Digital Marketing 2.0 - Rise of the predictive analytics

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 than crowdsourcing).

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 interactions.

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.