​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.