Probably the probability of redefining marketing is (Read as numeric 1. P (Redefining Marketing) = 1.) "One". The paradox is, that it is inevitably certain! It's high time we re-defined marketing and I have made a humble attempt to do so. Read on.
Marketing is a well-targeted, conversion-oriented, quantifiable, and interactive method of converting a prosumer into a consumer and vice-a-versa, thereby promoting new or existing products or services with the help of innovative technology as an enabler to predict needs and acquire and retain customers. That's easily said, however, it's a mix of storytelling, data analysis, technology, customer experience design, experimentation, systems thinking, and, of course, brand management; a combination of skill sets that may be hard to find.
A marketing scientist should be capable of understanding automation, data and emotions equally well to make it simpler. Will humans then, really perish as a result of AI? Absolutely not! However, it would definitely force the community to deviate from their conventional approach and take on a new way of working and life. A fully automated and integrated marketing platform should do the following:
• Gather Data,
• Plan and Automate and
• Increase value
While marketing scientists are capable of working with little to no data at all, with highly intuitive and psychological skills, they gather insights from experimentation like A/B testing to study content and its impact on behaviour. They use these tactics to render content based on dynamic segmentation and, obviously, that would be a "Segment of One".
Read Intuitive and Psychological skills as: "The machines may still need a human to do certain things that it can't do and therefore, the 'Future of jobs' may be at the dichotomy of Humanities and Science". It is a gap that our education system may have to rapidly fill in order to avoid urban depression and suicides. However returning to the marketing scientists, which may be the way to go, the scientific methods they use include the following:
• Framing Hypotheses
• Experimenting and Collecting Data
• Analysis, Inference and Conclusion
ALSO READ: Guest article by Ford marketing exec.
To 'Listen', in otherwise traditional Market Research terminology, is stated as to 'Observe'. Many marketers do not allocate a budget for listening - which imperatively means deploying an AI-based system to listen to existing and prospective customers on their needs across various channels which may include:
Web - to map customer journeys and tap behaviour which may include Frequency, Recency, Depth (Interest), Time, and Source and thereby arrive at a 'Purchase Intent' scoring. Any transactional data on their respective e-commerce engines would allow the recommendation engine to make the next best offer.
• Mobile App/Wallets
• Social Media
• Point of Sale (Includes Physical Store and Electronic Kiosks)
This, thereby, breaks the Data Silos and creates a 360-degree customer view or a true "Omni Channel" which today, only exists in the form of presentations while there are several tall claims.
Frame a hypothesis:
Develop a hypothesis which is deeply embedded in the target audience.
Traditionally, companies have been attempting persona development. However, reinstating an earlier said statement in the current context:
The probability of identifying a persona (P (Persona) = 1) is one. Having said that, it simply means no two personas are identical. Every customer is different and therefore, needs to be engaged differently. It's a simple approach that today's recruiters or talent analysts will certainly fail on. Hence, identifying the kind of marketing scientist that one will require, will be one of the biggest challenges of today and tomorrow.
Reason to buy - That's your story. The story would change from customer to customer; however, the value you offer may not change.
Measure - Deploy processes to measure in both qualitative and quantitative means.
Experimentation and Data Collection:
Experiment on channels, content, segments, spend, pricing, and packaging. This experimentation for a marketing scientist is not just limited to digital means like A/B testing.
ALSO READ: Essay by a behavioural scientist
Analysis, Inference and Conclusion:
This could be one of the most interesting aspects of a marketing scientist's job. A few examples of analysis and methods are mentioned below:
• Attribution Modelling - Optimise ad/channel spends based on the conversion goal paths and by assigning weightages to the sources.
• Cohort Analysis - Convert data into dollars by analysing customer groups across a variety of common attributes and create engagements specific to cohorts.
• Transaction Analysis - Convert visits into conversions by analysing potential product sales and create engagements specific to product groups.
• Product Analysis - Identify the strong and weak products and enable engagement via offers and coupons to the audience on a one to one level.
• Measure and fine-tune conversion goal paths - by reverse goal path analysis based on the last URL and timely interventions, by means of engagement, to avoid path diversion.
• Page Analysis and Heat Maps - Identify page performance to enable optimisation.
• Measure your content for effectiveness - Perform split tests or multivariate analysis to arrive at the right content.
• Enabling email automation for conversions - The email marketer can publish and track recipients to the website and their actions and automate response-based email marketing for effectiveness.
• Sentiment Analysis - Identify the social sentiment of your brand or events across social media. Identify the key trending influences.
Think in probabilities - that's one of the fundamentals required to pursue a career as a marketing scientist. For those of you who skipped your probability class, continue learning.
(The author is a senior marketer at Wipro)
For feedback/comments, please write to firstname.lastname@example.orgFirst Published : September 03, 2018 06:14 AM