Monday 5 December 2016

Ways to Improve Customer Lifetime Value (CLV) Using Analytics

Customer Lifetime Value (CLV) is the estimated net profit a business expects to get from the entire duration of its relationship with a customer. A high CLV is desirable as it implies increased profits and higher levels of customer engagement. Analytics is today used by mature organizations across the different stages of the customer lifecycle in order to increase CLV.
Acquisition:
While customer acquisition is a key performance indicator for many businesses, many companies fail to measure two important parameters – the Cost of Acquisition and projected Lifetime Value. Spending too much money acquiring customers with low lifetime values will corrode a company’s profitability.
This can be avoided by using look-alike modeling. In this technique, we profile existing high value customers across multiple dimensions and identify prospective customers that show similar patterns early in their customer journey. A 2014 study by eXelate showed that 50% of the advertisers who use look-alike modeling experienced a 2-3x higher lift in CLV.
Engagement:
Attracting a new customer can cost as much as 15 times more than retaining an existing customer,” says Terry Gillen in his book “Winning New Business in Construction.” It is a lot easier and cheaper in engaging existing customers. Apart from this leading to an increased CLV, it also influences brand perception positively. A few common analytics practices used to increase CLV by increasing engagement include:
a) Marketing based on Customer Segmentation:
• Demographics including age, income, gender. In the case of B2B organizations, factors like size of the business, industry could be used
• Purchasing patterns of customers
• Customer preferences and affinities
Once accurate segments have been created, marketing programs are created for each segment. For example, customers who have high purchase intent in buying back-to-school clothing could be offered a basic discount that could influence their purchase choice.
b) Cross-Sell:
By bundling relevant products together, cross-selling leads to higher engagement and value as it increases the breadth of the relationship. Item based collaborative filtering can be used to provide high impact, relevant cross-sell suggestions at every consumer touchpoint, rather than randomly bundling the items based on generalized usage assumptions.
c) Survey Analytics:
The results of surveys can be effectively used to update the usability of the products and tailor marketing messages to consumer preferences and needs. This leads to increased conversion, thereby leading to greater CLV and engagement.
Churn Prevention
Customer churn refers to the number of acquired customers who have become inactive. Churn impacts profitability significantly as the cost of acquisition has been borne without realizing profitability from the customer engagement.
By using analytics, every customer can be given a Churn Score. By building propensity models, customers with a high churn score – or the greatest probability of lapsing in a fixed period of time – can be identified. These customers could receive special marketing attention – more offers and increased customer support- to ensure that they remain engaged. This is especially impactful in increasing CLV when it is combined with high lifetime value scores.
Reactivation:
Re-engagement campaigns are an important marketing strategy. However, companies need to use analytics to identify which customers to re-engage with and with what messages/offers. Historically, re-activated customers bring in lesser value in the initial months after re-engagement. However, with the use of analytics, the difference in CLV between engaged and reactivated customers can be bridged significantly.
These are some of the more common ways in which global companies are using analytics to increase CLV. You can also look at how eBay uses data for competitive advantage.  To know more, email: marketing@latentview.com.

How The Bubble Blew: Using Social Media Analysis To Understand Declining Sales

Social media analysis is a powerful tool to understand trends. When trying to understand the reasons behind the declining sales of chewing gum, we turned to social media find the answers. We first studied the top usage occasions, when gum was chewed, in 2010. We then looked at those occasions to understand how they have changed. In each and every one of those occasions, we found alternate substitutes to chewing gum. For example, people chewed gum out of boredom in 2010 (6.7%). Smartphones have completely taken over this usage occasions, pushing chewing gum use to a negligible .3%.
Infographic on data analytics on chewing gum usage in USA
You may also find it interesting to read our blog on how social media is transforming the CPG and Retail industry.

Mastering Customer Retention Using Data

In the halls of marketing fame, customer acquisition stories get all the attention while those involving retention are rarely discussed with the same fervor and enthusiasm. Yet, most marketers will admit in private that customer retention is the single most important priority and an integral part of their strategy, which significantly impacts their bottom line. According to global consulting firm Bain & Company, it costs six to seven times more for an organization to acquire a new customer than to keep an existing one.
Increasing customer retention and loyalty is particularly relevant for retailers as they attempt to grab market share in a competitive environment. But how exactly can retailers identify and create loyal customers? Mere numbers — visits to a website, for example — do not tell us the full story of brand affinity unless they are also accompanied by other critical indicators regarding attitude or perception, and sentiment or advocacy toward the brand.
In creating a loyal customer, retailers need to first segment their customers based on their specific phase in the retention cycle. During each phase, data analytics can aid and support their outreach and marketing efforts.
blog-bad-cust-exp
When it comes to acquiring a new customer, retailers typically use a variety of channels to draw customers into their stores. These include advertising, promotions, affiliate programs, referral programs, coupons, rewards and other incentives. By effectively leveraging data analytics during this phase, retailers can ensure that these efforts are more than just random attempts at attracting customers.
How exactly can data help? Some of the ways analytics can be leveraged include better attribution insights or clues into what leads prospective customers into their stores. By identifying and gaining insights on better understanding the motivation of potential high value customers, retailers can redesign their promotions and product mix to attract such customers.
There are several ways in which a combination of internal and external data from CRM systems, transactional reports and demographic databases can be pieced together to get a better picture of what drives a customer to consider a purchase. When such information is combined with geographical data gleaned from mobile devices, it gives retailers a location-based map of customer sentiment and inclination. This can help them target customers with timely promotions and updates. For example, a customer could get a notification about a special discount on running apparel in a sporting goods store as soon as they step into a mall where that store is located.
Retailers often believe once a customer enters a store, online or brick and mortar, and make a purchase, they are a potentially loyal customer. They are entered into a database and the retailer begins treating this group (customers who have made a purchase) as a homogenous entity, running campaigns and offers to convert them to “loyal customers.” But they often do not meet with much success and have a very low conversion rate. The reason for this is customers all have different stages of brand involvement. To begin with, it takes a certain number of transactions before a customer and their purchases can be classified as intentional and not merely impulsive. What that number is changes from business to business, but a look at historical data can very quickly help define that threshold.
It is within this customer set, the ones that have reached the purchasing threshold, that you begin looking for “loyal customers.” Analytics can help create models based on historical data as well as external data, like wealth credit scores to predict high net worth customers, or common patterns of behavior, sometimes referred to as DNA markers. Customized campaigns directed at this segment will have greater success and higher conversions.
Ultimately, customer experience and satisfaction holds the key to driving repeat purchases. This is certainly more important in some sectors versus others. In the travel and hospitality sectors for example, positive customer experiences are essential for repeat transactions, while negative experiences could stifle this portion of business completely. Again, retailers can turn to data analytics in order to isolate bottlenecks and constraints that result in poor customer service or — in the case of an online business — cause customers to abandon their purchases.
As is clear from all of this, customer retention is a critical process that requires an understanding of where a customer or prospect fits in the retention cycle. With this understanding in place, data analytics can come in to help marketers cultivate the quality they most value in their customers — loyalty.

Thursday 1 December 2016

Analytics Maturity Survey

There’s no doubt that analytics can provide valuable insight to drive business value. But having the right analytics is just one part of the equation – enterprises must be able to capture and use it effectively.
When it comes to advanced analytics, how mature is your organization?
Take our free online assessment to find out – and see how you stack up against the competition. And be sure to get your free copy of “The Five Stages of Analytics Maturity,” which helps you recognize the five stages and provides recommendations to help you move forward to the next stage.

Monday 28 November 2016

How Conversion Rate Optimization Can Make or Break Your Sales Funnel

The Digital World
One of the defining trends in the recent years has been the colonization of the real by the digital. The rise of social media, smart phones, e-mails and blogging clearly indicate that the world is moving more and more towards digital.
With an increasing number of businesses going digital in order to reach more customers, empower them, reduce cost, and save time, they have a need to invest more on Digital Marketing activities such as Search Engine Marketing (SEM), display targeting, social medial advertising, email campaigns to increase traffic to their websites. While attracting customers has become easier, converting them and engaging them during their visit is still challenging and can widely vary depending on nature of business. Brands also need to think through the entire customer lifetime value and how data can be used to rethink the customer journey. We do have an interesting blog on how data can help improve success of marketing campaigns, which you may find useful.
Conversions and Optimization
Every aspect of marketing is entirely useless unless it produces conversions,” says Jeremy Smith, conversion consultant, in his book Marketing Land. A conversion can be as simple as an email newsletter subscription to account creation or making a purchase, downloading app etc.
Conversion rate is the percentage of visitors to the website who take an action. Businesses can boost their sales without increasing the marketing spend by optimizing the conversion rates. This can be done by evaluating the sales funnel of their website and improving its effectiveness. Conversion Rate Optimization (CRO) is a structured and systematic approach to improve the website performance and revenue, powered by actionable insights and user feedback.
Few key factors that conversion rate hinges on are:
• The relevance between the webpage content and the visitor’s expectation
• The Value Proposition being offered to the visitors
• The Clarity of main message and call-to-action
• The offers, tone and presentation that enable visitors to take action
Quality of conversions is essential
Optimizing conversion is important, but it is essential to understand that for many visitors conversion is a process. Businesses can aid the visitors to take an action by removing the barriers in their way, but ultimately, the visitors will have to take that final leap for a conversion to happen. The goal is not only to drive traffic to the website or manipulating visitors to convert, but also acquiring loyal and engaged users.
Avoiding the common mistakes
• Making the website cleaner and more modern might have turned out to be successful for one business , but does not guarantee the same for another
• Webpages designed to contain their information within a page, so that the user does not need to scroll can lead to uncertain conversions. Genuinely interested visitors will convert as long as the content is succinct and engaging
• Making changes to the website, without testing the importance of a particular page or page element in influencing the decision making of users, can degrade current conversions rather than optimizing it
• CRO done based on tests that ended sooner than required can turn out to be successful for a short term, but will fail over time, impacting the long-term engagement
Strategize and Execute
Businesses can strengthen their sales funnel significantly by following the below three-step approach:
Set Goals and Track:
Decide the conversion to be measured and optimized – generating leads, driving downloads, promoting products, starting a trial version or completing a purchase. Do not lose focus on macro goals such as revenue realization while optimizing micro goals.
The discovery of what matters is important as that will help figure out what to optimize and how. Use behavior analytics such as heat maps, visitor recordings, on-page surveys and form analysis to find what is stopping or enhancing visitors to convert.
While defining the conversion goals, businesses should also pay attention to other metrics such as visitor recency and visitor loyalty apart from measuring plain conversion rate. Customer lifetime value is a critical aspect to track as well.
Hypothesize and Test:
Form hypotheses and perform A/B Testing or Multivariate Testing based on the nature of business, type of goal and historical data. Sometimes it is also a good idea to request website feedback from customers. This will help in evaluate the bounce rate and exit rate at each stage of the conversion funnel.
Estimate the traffic needed and duration of test to achieve statistically significant results and stick to it.
Analyze and Iterate:
Evaluate the site loading speed for every page element change being implemented. Site speed is an essential aspect of optimization. Test results from a poor load quality site might be statistically significant, but the conversion results will be misleading as the traffic to the site is skewed
Starting with what matters the most, analyze each test result to uncover deeper insights into how users behave on website and the best way to guide them through the funnel. Test everything – just not all at once. Understand how different changes interact and impact the conversions.
While CRO can make your sales funnel by lowering customer acquisition costs and maximizing profits, it can also break the funnel when executed without effective strategy and there is always room for improvement. You may also find it interesting to read our blog on ways to improve customer lifetime value (CLV) using data and analytics or how eBay uses data for their competitive advantage..
The concepts discussed here are few of the common CRO scenarios involving analytics. To know more, email: marketing@latentview.com.

Sunday 27 November 2016

Analytics Driven Precise Targeting (APT): Streamlining Strategy to Target the New Age Consumer

Rethinking traditional consumer segmentation
The good old concept of customer segmentation, targeting and positioning framework, has stood the test of time and have helped marketers take strategic business decisions. These business leaders have routinely used demographic analysis to help craft a brand’s message, positioning, and marketing. By grouping consumers mainly by age, geography, ethnicity, gender, income and family status, marketers have been able to draw conclusions about that group’s shared interests and consumption behaviors.
But today, do new age consumers belonging to the same target segment, behave alike? Probably not.
By confining the consumers within the demographics conventions, we have instinctively assumed that everyone within the same group has similar needs. But in reality, consumers today construct their personality based on their interests, which in turn drives their behavior and consumption patterns. For example, according to trendwatching.com, in the UK, women now account for the majority of video game players, and there are more gamers aged over 44 years than under 18 years. Surprising, but true!
How should the marketers think differently?
To re-engineer their strategy, marketers need to understand why these new age consumers are not behaving as they should.
Listed below are some of the most common reasons:
• Access to ubiquitous information through technological advancements
• Diverse interest sets because of multicultural societies
• Ever demanding need for personalization
These factors have led to an unpredictable interest choices of the new age consumers. For example, there is a 40% overlap between the list of the 1,000 favorite artists for 60-year-olds and the 1,000 favorite artists for 13-year-olds, which points that demographic boundaries for music interests are getting blurred in today’s scenario.
Who are these new age consumer and how should we target them?
New Age Consumer Segmentation in 2017, using big data
How can a data driven approach, yield better results?
To make the segmentation strategy more effective and precise, marketers should understand the consumer’s needs and interests in a better way, and reposition their product around these needs. But in this dynamic market and fluid society, identifying these context-based segments can be a challenging task. This is where analytics comes in to the picture. Using a data-driven approach, marketers can understand what drives consumers within a particular need cluster and how to appeal to this segment group.
At LatentView, we help our clients take a data-driven approach to form a 360-degree view of their consumer. Our proprietary offering – APT framework, merges data and mathematics to identify the most appropriate distinct set of characteristics around which a product should be positioned. These characteristics are mutually exclusive sets of needs and each of these sets comprises different attributes that can help us understand what are the top needs and their associated drivers.
How it works?
To arrive at insights, we gather structured and unstructured data from surveys and social media. By leveraging social media along with surveys, we can quickly explore topics and discover many patterns, trends, and insights, without predetermining what we are looking for. This data is processed using advanced analytical techniques like lexical processing, attribute scoring, driver reduction and behavioral clustering to provide the list of needs, their context and relevant target segments. Some of the attributes that can be associated with the needs are:
• Related demographic
• Occasions and frequency of use
• Companions
• Time of the day
• Emotional drivers
• Functional drivers
We have observed that if we look at each of these sets holistically, we can reposition out products to align to these needs, thereby improving marketing effectiveness.
The journey towards insights
Generating consumer insights using data & analytics
We propose a three-phase process to arrive at these deep-dive insights:
Phase 1 – Gather relevant data:  When it comes to unlocking consumer behavior, the ideal approach is to use an amalgamation of both structured (e.g. survey) and unstructured (e.g. social media) data to understand drivers and associated attributes of consumption. It is recommended to conduct surveys over a large sample size which is representative of the entire population set.
Phase 2 – Do the math: Using advanced analytics techniques, process the unstructured and structured data to arrive at clusters which are mutually exclusive in terms of the needs they represent. These clusters can then be sorted based on the significance and size.
Phase 3 – Use business lens to evaluate:  Finally, these clusters are combined with business acumen to validate their relevance in terms of business requirements. In our experience, we have seen organizations merging several clusters which serve similar needs to develop their strategies around these clusters.
To summarize, adopting a needs-driven approach to segment consumers can achieve superior results than just demographic based demarcations.  It has been observed with many of our clients that, applying advanced analytics techniques on both structured and unstructured data yields more accurate results.
The concepts discussed here are few of the common targeting and segmentation processes involving analytics. To know more, email: marketing@latentview.com.

Infographic: Applications of Artificial Intelligence and Machine Learning in Business

According to a report by BofAMerrill Lynch, Robots & AI solutions market will grow to US$153bn by 2020, comprising US$83bn for robot and robotics, and US$70bn for AI-based analytics. Here is our view on how organisations can start implementing Artificial Intelligence and Machine Learning.
Infographic on Applications of Artificial Intelligence and Machine Learning in Business
 
http://www.latentview.com/blog/infographic-applications-artificial-intelligence-machine-learning-business/


How Can You Use Data to Rethink the Customer Journey?

In recent years, a lot of our conversation with our clients has revolved around how they can use data to optimize their customer’s lifecycle. Customer lifecycle is the entire path that a customer traces out and involves the following stages:
1. Target – Targeting involves delivering the right messages to the right people at the right time through the right channel
2. Acquire – Acquiring a customer involves convincing the customer of your brand and a promise to meet expectations
3. Onboard – This involves converting the prospects into your customers
4. Serve – This involves serving the customers in a collaborative environment by engaging them through feedback, ratings and by resolving issues, if any
5. Grow – This involves further strengthening the bond between your brand and the customer by recognizing customer loyalty and rewarding the same through special pricing and customized recommendations
6. Retain – This involves retaining the customer by reducing churns and working on regular feedback from the customer, while staying in touch through messages and providing regular updates.
Types of Consumer Data for Customer Lifetime Cycle
Optimal customer lifecycles result in greater Customer Lifetime Value, increased revenues and higher profitability. However, in order to optimize customer’s lifecycle effectively, companies first need to understand a customer’s journey.
Why is it difficult to track the customer journey?
With the aim of tracking the entire customer journey, we need different types of data to create a customer’s profile. Understanding a customer from all touchpoints, requires us to look at customer’s transactional data, psychographic data, demographic data, behavioral data, other forms of structured and unstructured data which is fragmented and often isolated. In the current scenario, there is no shortage of consumer data coming from various sources such as data from social media sites, data from a company’s own database, surveys, third party sources and data from other online activities. The challenge now is to collate all this data across innumerable and diverse touchpoints in order to track the entire customer journey.
Another challenge is to map this entire customer journey to create a visual representation of this journey to be able to take meaningful decisions based on the insights. Customer journey map can help to tell the story of your customer’s experience at different stages. Mapping the customer journey can also help understand more complex customer journeys easily and make wise business choices.
How can analytics help map the customer journey?
With innumerable data sources and hordes of information, both structured and unstructured, analytics can help deliver more deep-dive insights and provide insights at a more granular level. Capturing such detailed information at this level has been possible due to the digital transformation in the past few years which was not possible before. While there is a sea of data and information available online, there are still gaps while tracking the entire path of the customer journey.
Customer Lifecycle Chart for Retailers & CPG IndustryThis is where analytics comes into play. Analytics can play a vital role in helping marketers better understand the idiosyncrasies of each consumer and enhance the overall consumer journey accordingly. In return, consumers are able to get personalized information and discounts according to their interests instead of irrelevant spam – a win-win situation. Due to the ever-changing consumer mindset and consumer paths getting more complicated with time, we can use advanced methods like Process Mining to better understand behavior patterns of our consumers.
What is Process Mining?
Process Mining (PM) is a process management technique which allows us to analyze processes based on event logs. Since data is everywhere, most companies have loads of unused data. There are different types of data about the consumer like transactions data, behavioral data, geographical data etc. which can help an organization understand the consumer through different touchpoints. This technique helps understand that data at a granular level and can help trace the entire consumer journey.
There are three types of use cases in customer journey mapping using Process Mining:
1. Discovery – This technique takes web analytics data (from analytics platform like google analytics) and produces a model.
2. Conformance – In this technique, each instance of the customer journey is mapped to the predefined ‘golden path’ and we check for deviations of the customer from the same.
3. Enhancement – In this technique, we improve an existing process. Enhancement helps to extend the capabilities of the model by predicting customer behavior on the website.
In a website, Process Mining can help trace the entire consumer path and give insights on which pages are better influencing the customer, which pages are the consumers liking, at which pages are the consumers dropping, at what point are the consumers more likely to abandon, etc. It will also show you where the customers are on their journey, which will help marketers take a more informed decision about how to overcome these challenges and provide real- time, instantaneous assistance to the customers.
Used correctly, Process Mining will be able to deliver deep-dive insights and a more granular understanding of the entire consumer journey. It will help target the customer at the right time, with the relevant information, thereby delivering a superior, customized customer experience.
*With inputs from Alka Pandey.

Saturday 19 November 2016

How Can You Use Data to Rethink the Customer Journey?

In recent years, a lot of our conversation with our clients has revolved around how they can use data to optimize their customer’s lifecycle. Customer lifecycle is the entire path that a customer traces out and involves the following stages:
1. Target – Targeting involves delivering the right messages to the right people at the right time through the right channel
2. Acquire – Acquiring a customer involves convincing the customer of your brand and a promise to meet expectations
3. Onboard – This involves converting the prospects into your customers
4. Serve – This involves serving the customers in a collaborative environment by engaging them through feedback, ratings and by resolving issues, if any
5. Grow – This involves further strengthening the bond between your brand and the customer by recognizing customer loyalty and rewarding the same through special pricing and customized recommendations
6. Retain – This involves retaining the customer by reducing churns and working on regular feedback from the customer, while staying in touch through messages and providing regular updates.
1
Optimal customer lifecycles result in greater Customer Lifetime Value, increased revenues and higher profitability. However, in order to optimize customer’s lifecycle effectively, companies first need to understand a customer’s journey.
Why is it difficult to track the customer journey?
With the aim of tracking the entire customer journey, we need different types of data to create a customer’s profile. Understanding a customer from all touchpoints, requires us to look at customer’s transactional data, psychographic data, demographic data, behavioral data, other forms of structured and unstructured data which is fragmented and often isolated. In the current scenario, there is no shortage of consumer data coming from various sources such as data from social media sites, data from a company’s own database, surveys, third party sources and data from other online activities. The challenge now is to collate all this data across innumerable and diverse touchpoints in order to track the entire customer journey.
Another challenge is to map this entire customer journey to create a visual representation of this journey to be able to take meaningful decisions based on the insights. Customer journey map can help to tell the story of your customer’s experience at different stages. Mapping the customer journey can also help understand more complex customer journeys easily and make wise business choices.
How can analytics help map the customer journey?
With innumerable data sources and hordes of information, both structured and unstructured, analytics can help deliver more deep-dive insights and provide insights at a more granular level. Capturing such detailed information at this level has been possible due to the digital transformation in the past few years which was not possible before. While there is a sea of data and information available online, there are still gaps while tracking the entire path of the customer journey.
2This is where analytics comes into play. Analytics can play a vital role in helping marketers better understand the idiosyncrasies of each consumer and enhance the overall consumer journey accordingly. In return, consumers are able to get personalized information and discounts according to their interests instead of irrelevant spam – a win-win situation. Due to the ever-changing consumer mindset and consumer paths getting more complicated with time, we can use advanced methods like Process Mining to better understand behavior patterns of our consumers.
What is Process Mining?
Process Mining (PM) is a process management technique which allows us to analyze processes based on event logs. Since data is everywhere, most companies have loads of unused data. There are different types of data about the consumer like transactions data, behavioral data, geographical data etc. which can help an organization understand the consumer through different touchpoints. This technique helps understand that data at a granular level and can help trace the entire consumer journey.
There are three types of use cases in customer journey mapping using Process Mining:
1. Discovery – This technique takes web analytics data (from analytics platform like google analytics) and produces a model.
2. Conformance – In this technique, each instance of the customer journey is mapped to the predefined ‘golden path’ and we check for deviations of the customer from the same.
3. Enhancement – In this technique, we improve an existing process. Enhancement helps to extend the capabilities of the model by predicting customer behavior on the website.
In a website, Process Mining can help trace the entire consumer path and give insights on which pages are better influencing the customer, which pages are the consumers liking, at which pages are the consumers dropping, at what point are the consumers more likely to abandon, etc. It will also show you where the customers are on their journey, which will help marketers take a more informed decision about how to overcome these challenges and provide real- time, instantaneous assistance to the customers.
Used correctly, Process Mining will be able to deliver deep-dive insights and a more granular understanding of the entire consumer journey. It will help target the customer at the right time, with the relevant information, thereby delivering a superior, customized customer experience.