The real utility of big data comes to life when we are using it to analyse hidden patterns, analyse correlation and predict the possibility of future occurrences. This has direct implications to how marketing can use big data as a powerful tool to enhance the consumer experience, drive efficiency in spends and improve their effectiveness.
 
The promise of Machine Learning is to help duplicate the human experience of learning by extrapolating from past behaviors to predict future behaviors. The difference will be the scale/ speed / accuracy and consistency – all of which will improve exponentially. Machine learning, because it works towards determining patterns (Vs pre-set algorithms that are set to solve a specific problem logically) is especially useful in ‘real-time’ scenarios (the better way to talk ‘real-time’ is actually ‘right time’ given that business cycles vary depending on category) where the time available to discover the pattern and attempt predictions is extremely limited. It works best when you want to understand what happened in the last week and what is likely to occur in the next week.
 
Before talking any further, I’d like to step back and underline the key imperative of big data – actionable insights. Insights that are actionable within the business cycle of the predictions. Insights that are deterministic, help generate clear conclusions or hypotheses and will be accepted by the broader organisation as action-worthy.
 
Here are some pointers for marketers towards helping big data predictions :

1. Start small : Building a robust big data model and making it stable (wrt data feeds/ predictions) is part art, part science. It will take a few experts within the organisation (Data Science/ Analytics/ Category Experts/ Sales) to provide insights to help stabilise the model at accuracy. Learning is messy and the initial parts of gaining momentum can be confusing. You don’t want large teams to be consumed by the learning curve.
2. Point North : Are the predictions aimed at helping achieve the top business goals ? Identify the prediction results to the goals, have a clear template for analysis (combining the prediction with human expertise to creating solutions), the process (frequency, time to market, responsibility, budgets).
3. Right Time Vs Real Time : Every business cycle varies and has a different rhythm of going to market. Organisations have unique challenges in decision-making, approvals processes and executing investments/ making changes.
4. Right Data Vs Big Data : In finding the signal you need to help eliminate “noise”.  Big data does not mean all data. This is where the internal expertise within the company can truly add value in helping teams make decisions to ignore certain datasets for specific predictions.

 
Predictive analytics for marketing decisions is an emerging art, enabled by science. While its engine is driven by the past the true potential lies in taking the leap from predictions to imagine new possibilities.
 

Assuming you have worked the right target and the right context how do you deliver the right message that’s exactly relevant to the consumer and that has a higher certainity of converting ?
 
Given that achieving relevance in a mass precision setting is a complex task the following steps can help :
 
1. Segment Understanding : The inputs into creating programmatic creative start with an understanding of the consumer segments that will be targetted and their specific behavior/ desired behavior changes. Understanding their consumer journeys and the inherent opportunities for behavior change within each episode of the journey becomes the opportunity statement that creatives are trying to solve for. The different journeys and triggers per segment creates the first level of personalisation.
2. Context of the Segment / Targetting : Context creates the second level of personalisation. Context is information regarding the platform, time, device, location, weather and editorial environement in which the creative will interact with consumers. Especially, understanding the unique platform specific interactions and preferences of users is key in improving relevance.
3. UX : The third and sometime the most important level of personalisation is with the UX design of the creative and its possible iterations based on A/B Testing and optimisation. This includes technical considerations like image compression, kight-weight animation and reducing custom fonts. Reduction in load time is a parallel goal for optimal UX.
 
A few examples to showcase the simplicity of the creative solutions within programmatic :
 
1. The Economist – Personalised Display : As The Economist looked to add new subscribers it wanted to target people who wanted more value / analysis when reading the news.  First it chose content that was most popular with its current user base and understood the context of the article related to the user profile. Second it targetted news websites based whose context was similar to the ones preferred by its loyal consumers. Third, to create relevance it created witty and humorous content and placed it next to the relevant news articles (always linking its page context to user profile). The campaign saw The Economist increase prospects by 650, 000 and an ROI of 10:1.
 
2. O2 Refresh – Personalised Video: The goal was to increase conversion for 02’s latest refresh campaign amongst the 3 segments of early adopters, out of contract and users with upcoming contract expiration. It used data (device, location) in order to effectively target its segments with personally relevant messages (recycle value for their current device + upgrade options + popular preferences of users like them + store locators). O2 recycled its TV ads to digital to benefit from precision targetting and developed programmatic creative to help them achieve significantly higher results (CTR increased by 128%).

If you are starting your journey as a marketer using big data and wondering about the specific applications of predictive analytics and how it could impact your marketing decision making process here are a few ways to think about it.
 
The key imperative of embarking on the journey into big data and predictive analytics is “actionable insights that help achieve marketing goals”.  Predictive analytics using machine learning will help you extrapolate past behaviors, reveal patterns and help predict future behaviors.
 
Simple Applications  

1. Lead Conversion : The life-blood of sales is lead conversions and its common sense to conclude that not all leads have equal potential to convert. Predictive analytics can help you identify from past conversions the ideal demographic/ behavioral characteristics of a strong lead vs a weak lead. Prioritising leads in this way will help sales teams create a plan for engagement based on immediacy of conversion.

Note : Weak leads might be dropping off in a consistent pattern that might require a different set of actions to be performed to help convert them into strong leads.

1. Churn Prevention : Retention is always more profitable Vs acquisition. Customer Lifetime Value is the cornerstone of consumer centric marketing. Therefore, reducing churn by determining the factors that lead to churn of specific segments of consumers and creating plans to proactively prevent churn is one of the most useful applications of predictive analytics. Likelihood to churn can be based on visible factors like complaints lodged, frequency of interaction with the product, time of year, weather, demographic shifts or by a series of complex factors and invisible factors like competitive pricing, in-market innovation in features etc.. Based on the reasons for churn we can create the right ‘nurture’ programs to address the segments.

 
Strategic Applications

1. Identify High Value Segments : Identifying high value customer segments is important in two ways – helps you grow the profitability per customer metric improving your ROIs and it helps you understand success metrics that can be scaled across segments. Predictive analytics will help you understand the likely behaviors/ spend capacity/ spend propensity per product within the high value segments. It will also help you identify the tipping point for high value segments allowing for scaled conversions.
2. Up-sell / Cross-sell : Data of consumers who have recently traded-up and across offer insights of who/ when/ how they have done so. Consumers choosing a time of year (sale or festive season), usage of loyalty benefits, increased usage frequency etc will point to certain trigger points that lead to their change in behavior. This could come from a change in lifestyle, lifestage, season, income shifts etc.. Matching internal consumer data with external 3rd party data might be necessary to provide a clear picture of when/ how they have traded-up/ across. 

 
There are more than 4 applications for predictive analytics (Content Mapping, Programmatic etc) but the list will help start the right practices to help ensure early wins within the orgainsation.
 

​A central part of consumer centric design is constructing consumer journey maps – the journey of a consumer to purchase a product-service as experienced by them. Agnostic of a brand imposed division of its experience into departments, offline-online, applications, enquiries, help-desks, customer service, sales, after-sales etc..
 
When building a consumer journey map its easy to fall into the trap of isolating the touch-points the consumer interacts with the brand and begin to reconstruct the experience within each touch-point. This leaves the organisation with blind-spots as its ignoring the intermission periods when the customer is still engaged in the purchase process but invisible to the brand. Research (search, reviews, retail visits, browsing, subscriptions to newsletters), information gathering (for technical expertise, customer service calls), deal hunting (comparison shopping on specialist sites, financing, opting in for alerts to discounts) are all part of the purchase process – each taking a different duration and having a unique weightage on the final decision. This customer reality requires the organization to expand its thinking to include their entire journey and map it based on ‘needs, motivations, assumptions and expectations’ of the customer at every phase.
 
How do you construct customer journeys using the design principle of empathy?
 
Step 1 : Discover

1. Representative : Capture high traffic points. Use customer data to understand the high density in visits, pass-throughs, used utilities and services on your website-retail location-apps. High traffic represents high customer need and could unearth organisational bottle-necks.
2. High-Value: Its likely that the top 2-3 segments are more desirable to plan towards Vs all. Choose segments that are robust (value/ volume that helps meet the organisational goal) and differentiated in behaviors (high Vs low usage, bulk Vs piece-meal buy, discount Vs premium quality seekers etc).
3. Insightful: Actual observation of the segments journey with attention to tasks, barriers and completion time-ease should be supplemented by qualitative customer research in the form of focus groups and 1x1 interviews. This will help identify the list of needs-motivations-barriers in the journey. 
4. Actionable : For the high value segments, its important to develop a hierarchy of needs (importance to customer) x current state (satisfaction). Classifying the needs based on simplicity (skill required), ease (time + effort investment), control (personalization, customization), speed (response time), transparency (information sharing), trust (safety in transactions, guarantees, privacy) will also help in actionability. 

 
Step 2 : Define
1. Segment Journey by Episodes : Each segment of customers follows a unique series of steps towards purchase. Every step is an ‘episode’ within the entire journey having unique actions that start-stop to link towards the next episode (Search by itself is an episode in the consumer journey but might begin in social media-reviews and stop at brand website). Identify the steps using various data points available to the brand – search, web visits, service centre calls, CRM database, social media. Understand that their start/ pause points will vary. Also, segment behaviors will converge at critical steps (booking, coupon downloads) and diverge based on their specific needs (e-commerce purchase Vs offline). Remember – the context (time / geography / event) plays an important role in understanding journeys. Certain journeys repeat at consistent times of the year (a Christmas journey) while others are live for a specific event and repeat only of that event repeats (hurricane related purchases).
 
2. Identify Channels per Episode  : Each episode will be connected to a few channels (in-store, customer service, eCommerce site, website etc). List and weight each channel based on its importance in the consumers goal for that episode. Don’t ignore the multiplicity of channels in each episode.
 
3. Episode Trigger : Every segment moves a step closer to purchase because of invisible/ visible ‘triggers’ that motivate them to do so within each episode. Its important to identify the trigger and understand its role. Example: when a customer is searching for specific solutions and the brand appears top of the search results with relevant content the search result is a trigger for the consumer to move to the next episode in their journey. This understanding will ensure that the trigger is robustly created – the absence of the search result will only create an undesirable pause in the consumer journey.
 
4. Duration + Context : Every episode has a duration that could vary between segments. Example : for laptops, the discovery duration for technophiles could be very short while for others could last weeks. Duration and context of the duration is an important step to understand in-depth because it contains opportunities for brands to leverage and add utility. If we understand that the post discovery episode of ‘browsing-comparison-reviews’ is taking longer for a certain segment Vs others shortening the duration could significantly impact the conversion – simplification of user experience, enabling easier discovery, quick and helpful customer call backs, chatbots etc could help improve the user experience and motivate quicker conversion.
 
End note : Consumer journeys and segments are in a constant state of flux because of the introduction of new technologies, change in the environment and new expectations in experiences. Constantly R&D new segments, identifying new niche segments, discover new behaviors and therefore create new triggers. 

The goal of customer centric design is achieving customer delight.
 
Designing for an emotional end state like delight means you need to answer for identity (who to delight), measurability (what is delight) and specificity (how to deliver delight) in the brand experience.

A. Identity (Who to Delight):

1. Identify the High Value Audience: The economic value of a customer retained over a long period of time far outweigh the costs undertaken to delight them. However, not all customers are created equal and organisations have to think about monetization while identifying ‘who’ these valuable customers are. Data sources can be transaction data, direct response mailing lists, web site visits, app user profiles etc.. Two methods that help choose ‘high value audience’ segments:

• ‘Recency, frequency, monetary’ (RFM Method) : Works on the 80/20 Pareto Principle that you derive most of your revenue from a small sub-section of your customers. Commonly used as a direct marketing tool but can also be used to help isolate the pocket of segments that show extreme behaviors (High Vs Low Recency-Frequency-Monetary combinations) and the difference in the segment behaviors will point to design improvement opportunities.
• Customer Lifetime Value :  Useful when the business depends on recurring revenue from the same customer and gaining new customers is expensive (Subscriptions, memberships, accounts).

2. Map Consumer Journeys: Understanding this high value audience journey is the next step. Empathy for the pain / pleasure points and identifying key trigger-points (that lead directly to conversion) where we could create frictionless journeys helps us create a template for the ideal consumer experience. This audit, done with deep inputs from multiple departments that are consumer facing, covers processes/ offline-online/ rules & procedures/ inter-departmental prioritisation and requires transparent data sharing internally. The entire organisation should have the capability to ‘walk in the customer shoes’ and contribute to making changes that will improve the customer experience in every way.

B.  Measurability (What is Delight) : What are the right metrics to measure design goals that are consumer emotional end-state resulting from their overall product-service experience ? In order to arrive at simplicity in goal definition the brand needs to have derived customer motivations- need states- expectations –task completion episodes. This leads to the emotion that the brand wants the consumer to have when it has solved for their need.

When Airbnb defines its goal as “To make people feel that they could belong anywhere” they are trying to solve for people who want to travel without feeling like a tourist. They would like for their ‘guests’ to feel at home, in a stranger’s home. This a simple, specific but inspiring design goal and begins to point towards the changes that need to be made in order to meet the goal. From educating hosts and training them on how to personalize the experience and helping guests live ‘like a local’, expanding the list of locations, matching for host-guest ‘tastes and preferences’ Airbnb is operating with the design goal in mind.      

What are the right metrics that help measure delight? Examples of a few metrics :

1. Satisfaction: Basic but works very well across industries. Surveys can be short and effective (Satisfaction, Task Completed, Reasons for Incomplete Tasks)
2. Customer Effort Score: if complexity is a key pain point in the category (Banking/ Insurance/ Automobile/ eCommerce) measuring how much effort was needed by the customer to complete their goals is a relevant metric.
3. Net Promoter Score: when the human component of the service is a large part of the consumer experience (Hotels, Restaurants, Retail)

 
C. Specificity (How to design for Delight) : This part of the actual design is iterative and will involve ‘design-prototype-test-design’ steps. Think of the design solutions with the specific goal in mind and through the following filters:

1. Eliminate ‘Pain Points’ : Where, when and how can these be eliminated ? Introducing simplicity, seamlessness, bite sized information and customer control of the experience helps address many of the pain points in the customer journey. People training, internal process simplification and use of technology combinations are required to standardize the design changes. Example : In September 2017, Ikea bought-over TaskRabbit (marketplace for 60, 000 workers who perform household repairs-fitting-handymen tasks) in an effort to eliminate the pain felt by consumers post-purchase of Ikea’s products. It has introduced a VR Platform (Ikea Play) that helps customers see how their furniture will look ‘in situ’ in their homes eliminating a pain point felt by customers who find it difficult to use their imagination to do so.
2. Elevate ‘Pleasure Points’: The moments of true delight should be retained or enhanced when making the design changes. In fact, building the design from the ‘pleasure points’ outwards helps to highlight the core differentiator of the brand.
3. Trigger Points : Points during the customer journey that lead directly to a conversion. ‘Buy Now’ button, ‘can we help you’ chatbot, ‘fix an appointment’ / ‘sale’ sections, ‘1 click ordering’, ‘Dash Buttons’ (Amazon) are examples of triggers. Ensuring that customers have full access to the ‘triggers’ at all times in their journey and that there are no distractions surrounding the triggers is extremely important.

Great insights are the start point for strong marketing plans and strategies. Insights (an unobvious truth about your consumer) are rare and a reward for combining robust data analytics with product usage observations, consumer experiences, deep domain knowledge and lateral linkages between consumer life motivations and brand intersects.
 
As a first step investing in the collection, analysis and modeling of big data is prudent. But data the journey to insights does not stop here. There is a need to dig deeper into consumer motivations, behaviors, product usage and experiences to reveal ‘actionable insights’ around which marketing strategies and plans can be built.
 
How can we convert big data into action insights ?
 

1. Understand the Context of the Business Goal : Before you begin the journey into actionable insights the problem definition – ‘what are you solving for?’, has to be tightly defined along with the context. “Brand X eCommerce sales have underperformed by 10% Vs Goal in Q1 (Business Problem) in the period that the promotions calendar has been modified/ pricing and marketing spends remaining fixed (Context) – where is the leaky bucket and what insights can we find to help sales recover by Q3?“.
2. Start with Small Data : With a tightly defined business problem pruning the data feeds to narrow the field surrounding the immediate problem helps. Think brand inputs-consumer trends-competitive actions-technical variables.
3. Segment : Data needs to be split into cohesive segments with common attributes to understand if there is a peculiar shift in one segment / a few segments that have contributed disproportionately to the business problem.
4. Answer for ‘Why’ : Every delta in data points to a behavior change. Every behavior change has a reason ‘why’. Answering for ‘why’ will then lead you isolate specific reasons, improvement areas and gaps that are actionable next steps.
5. Look for the Customer behind the Data: Not everything that’s true is revealed by data alone. If ‘why’ cannot be answered then its time to ask an actual consumer, observe and understand hidden motivations or patterns that lead to behavior change.

 
Gleaning actionable insights requires one last step – actioning the insight. The ability and willingness to action the insight findings balancing the resource constraints of  (time-money-opportunity cost) should be determined before you start the process of discovering the actionable insights. Amongst the list of resource constraints the one that tips the balance in more occasions than others is usually – time. Time taken for the analysis and decision making overflows into the time needed to start actioning the insights.  So be sure of the ability to action the insight before searching for the action insights.

We all thought that programmatic advertising would solve all of marketings problems – personalization, engagement and boom (!)- conversion. Apart from the many problems its faces in execution (transparency, data quality, ad fraud etc) the issue seems to be the lack of focus – more talk-time is spent on targeting and viewability (these are milestone not metrics) while programmatic is meant to improve purchase intent/ aid conversion.
 
Given that programmatic spends will grow to $50 billion in 2017, improving quality in programmatic has to be an imperative if marketing is to benefit from the efficiencies in automation @ scale that it promises.
 
The fault is not with programmatic advertising itself, its in what rules we preset for programmatic – rules that are less focused on contextual logic/ conversion but more blandly robotic in executing using remarketing principles. As simple as it sounds ‘right person, right time, right content’ is anything but.
 
How should we choose the preset rules ? Using machine learning might help us create these rules by keeping conversion as the key goal and allowing for machine learning to match time/ audience demographics/ contextual content / advertising content to goals and determining the patterns that work and those that don’t.
 
2 patterns that machine learning will map for you :
1. Identify your high value audiences : using past behaviors, expressed current signals, probability of conversion and the average Customer Lifetime Value (CLV).
2. Identify your high conversion content-context : the golden combinations of context + content that leads to conversions with the high value audience.
 
Patterns are not static and change over time of year/ influenced by trends and events as well as the entry of new audiences/ exit of current customers. Focusing on conversion oriented goals in programmatic and using machine learning to develop strong conversion models will help sharpen the efforts of targeting-viewability-content creation.  
 
One innovation that is allowing transparency and goal conversion to become the focus is ‘post trade programmatic’ – where the buyer can read 300-400 signals of the delivered impression to determine the accuracy-efficacy of the buy, including targeting and viewability. This then allows for machine learning to kick in, analyse the delivery Vs goals to determine success/ failure paths and optimise the buy every time.
 
Using machine learning to help improve programmatic performance is not the only solution to achieving better quality programmatic but it certainly begins to immediately improve user experience, targetting accuracy, transparency and content choice.

Basic segmentation exercises like demographic segmentation have their usefulness in understanding large groups while psychographic segmentation helps in linking groups of people based on their mind-set and motivations. 

But as we move deeper into building relevance and realising the potential of mass precision targeting, value exchange and enabling desired action segmenting based on common behaviours becomes a step towards building  higher relevance and actionable marketing plans. Behavioral segmentation will help marketers ensure that their digital plans target the right consumers, engage with the right content that aids conversion – behavior change at scale. Behavioral segmentation promises ‘Mass Precision’.
 
To achieve the mass precision promise of behavioral segmentation we need to do the following:

1. Move from Attention to Action : Desired behavior change (actions) are the end output that digital investments will be measured against. Actions that lead to conversion or that are probable proxies for conversion in the future. Buying an airline ticket might be an immediate conversion action but searching for holiday locations, visiting comparison sites for travel bookings, subscribing to an alert for travel promotions etc indicate the propensity for future behavior change and are good proxy actions.
2. Create Action based Segmentation : Using the behavior change/ action proxies as a filter derive segments that are similar in action but differentiated in the volume, value, context of the action. Within the defined set of consumers who have performed the action/ proxy actions how do they break out into clusters that are different from each other ? Segment clusters will naturally break out on volume/ value parameters but other likely clusters based on destination/ time of year/ type of travel/ adjacent bookings (hotel + airline + type of tour + car rental) will reveal segments that are opportunities for further investment. 
3. Chart the Consumer Journeys : The answer to ‘who does what action and why ?’ is what we are trying to ascertain. The order and cascading prioritization of actions will vary across segments. Internal data (current consumers, visitors to website, subscriptions), 3rd party data (search, social) needs to be blended with a qualitative understanding of the consumer to chart the different segment journeys. Its less important to be precise than to have answers to the key tipping points and barriers in the consumer journey with an in-depth insight into the ‘why’ behind the key actions. Behavior change is the objective and Consumer Journeys are rough guidelines that help interrogate the data and refine the investment choices. In most cases, it will inform organisations about changes that need to be made to improve user experience and remove friction in conversions.
4. Small Data Analysis : Having isolated segments its time to use small data focused on certain key actions (Discovery, Intent, Conversion, Abandonment) to hone in on specific solutions for behavior change by segment. Prioritization based on segment size, actionability and urgency will be an output of this analysis.
5. Pilot-Optimise: Time to action the investment choices as pilots, observe if the hypotheses in the analysis is verified in changing behavior within the segments.

 
Behavioral segmentation is a dynamic exercise as consumer preferences change, actions change in response to context. In order to keep ahead of these changes the next step in behavioral segmentation becomes a serious investment in machine learning/ predictive analytics. Using big data modeling to discover new opportunity - new segments, new actions. 

With multiplicity of choices for consumers  - platforms x applications x devices x newsfeed refresh x e-mail alerts x ad messages, it seems that consumer attention is a scarce resource. But attention is the end product of a ‘time-value’ correlation (I will spend more time and pay attention for more value) and not a trade-off (I will pay more/ less attention to brands).
 
Value is the inflexible determinant of consumer attention, not time. Consumers will reward you with their attention if there is the likelihood of a value exchange (attention for entertainment, interest for information, visit for experience).  One strong candidate for a value exchange is ‘purpose’ (purpose for participation). When customers see an alignment in their values with a brands actions, they will begin to disproportionately invest their time participating in the brand via attention, purchase, advocacy.
 
A brand acting on its purpose is not altruism. The argument for brands to build or re-discover purpose is not emotional, its economics.
 
A. Profit : When Always ‘Like a Girl’ decided to speak against gender stereotyping its talking about a subject that’s extremely relevant to its core consumers – teen girls/ young women. It lit the spotlight on the issue of ‘confidence’ in young girls, making the conversation mainstream while allowing its consumers to participate and shape the discussion. But, as importantly, by using the connection of puberty to the loss in confidence amongst young girls it puts its brand promise of ‘protection during your period’ in focus. 76 million views, 12 billion impressions, +50% increase in purchase intent and +1.4% share point increase later Always proves that brands that combine purpose with product build profit.
 
B. Relevance : It helps a brand quickly align itself with like minded people telegraphically. “I believe in what you believe” is more powerful than “I understand you” and definitely more compelling that “I have something to sell you”.  AirBnB “Belong Anywhere” builds brand purpose and thereby relevance. Respect for its community has been the bedrock for AirBnBs actions and its paying rich rewards in the form of free marketing. More than 77% of AirBnBs content on Instagram is UGC and it has contributed directly to an uplift of 17% in its followers. AirBnB continues to live true to its purpose at every opportunity. Its “Belong Anywhere” idea has been activated in many ways – earlier this year during the Trump Innauguration, Super Bowl 2017  (#weaccept) and most recently in Australia with a message promoting marriage equality (Until We All Belong).
 
C. Reach
In low involvement/ high media cost categories like FMCGs attaching meaning to a brand is a strategically sound method to make your media work harder-longer for you. Laundry brands like Ariel “Share the Load” have embraced the route of brand purpose and made it a part of their mainstream communications. Sales lift attributed to the campaign was 5% with 1.5 million pledging to ‘share the load’ with their wives in India.
 
D. Captive High Value Audiences : For its Model 3 launch Tesla operated on a $0 marketing budget and clocked $14 Billion in pre-orders in 1 week ! People wanted to participate in the brand, wanted a slice of the future for themselves so much that they paid Tesla a year in advance to buy their car.
 
The argument for purpose led marketing is growing stronger. When done thoughtfully and consistently it plays a strategic role in helping brands capture the hearts and wallets of its targeted consumers.