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Analytics -- The driving force behind online retail

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Analytics-- The driving force behind online retail

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EXECUTIVE SUMMARYOnline retail has changed the face of shopping for good, and true to its reputation analytics has paved the way for this revolution to take place. Retailers now have the opportunity to understand customers and cater to them in a manner like never before. But what exactly does analytics do that makes online retail thrive? How do the marketers behind the scenes utilize analytics in such a way that it can grant value to their customers and at the same time maximize their share of wallet and revenue? This white paper is aimed to offer an introduction into retail analytics and provide a few high level examples of how Online Retail can strike gold using analytics.

RETAILERS NOW HAVE THE OPPORTUNITY TO UNDERSTAND CUSTOMERS AND CATER TO THEM IN A MANNER LIKE NEVER BEFORE.

Knowing what to cater

WHY IS ANALYTICS SUCH A BIG DEAL IN ONLINE RETAIL?The age old retail model is dying a slow death. We are now in the age of the instant customer, who has neither the time nor the patience for a traditional shopping experience any more. Gone are the days of making your way to tightly packed commercial areas, hours of shopping for the best deals and waiting in endless queues. Customers have moved on and so has the majority of retail - onto online realms.

WE ARE NOW IN THE AGE OF THE INSTANT CUSTOMER, WHO HAS NEITHER THE TIME NOR THE PATIENCE FOR A TRADITIONAL SHOPPING EXPERIENCE ANY MORE.

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This surge of online retailing has changed customer experiences to a level that was unimaginable just a few short years ago. Retailer- Customer interaction is now present at every turn and in the quest for omni channel superiority has also spread across channels and devices and unsurprisingly is not limited to only certain products or goods.

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CLOTHING BOOKS ELECTRONICS

In fact, a recent study of consumer online purchase behavior identified that Consumer Electronics, Books and Clothing were the top categories of merchandises purchased online. The percentage of consumers purchasing the above category items online numbered at 69%, 67% and 63% respectively of the total purchases.

Hence, every piece of customer engagement is producing incredible stores of data (analytics) which holds key knowledge about customers and their behaviors. Getting to know customers at such a level of granularity was nonexistent in traditional retail which severely limited the ways that customers could be reached out to. But analytics has turned the situation around, allowing retailers to get a complete understanding of the customer psyche. Every intent, action or inaction can be visualized via analytics to be scrutinized and made use of to manage customers better. And this, makes analytics a big deal in online retail.

Top online purchases

This surge of online retailing has changed customer experiences to a level that was unimaginable just a few short years ago. Retailer- Customer interaction is now present at every turn and in the quest for omni channel superiority has also spread across channels and devices and unsurprisingly is not limited to only certain products or goods.

In fact, a recent study of consumer online purchase behavior identified that Consumer Electronics, Books and Clothing were the top categories of merchandises purchased online. The percentage of consumers purchasing the above category items online numbered at 69%, 67% and 63% respectively of the total purchases.

Hence, every piece of customer engagement is producing incredible stores of data (analytics) which holds key knowledge about customers and their behaviors. Getting to know customers at such a level of granularity was nonexistent in traditional retail which severely limited the ways that customers could be reached out to. But analytics has turned the situation around, allowing retailers to get a complete understanding of the customer psyche. Every intent, action or inaction can be visualized via analytics to be scrutinized and made use of to manage customers better. And this, makes analytics a big deal in online retail.

BILLING

The break away factor

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HOW CAN ANALYTICS HELP IN ONLINE RETAIL?So if analytics is the real deal, how does it work? In what forms does it contribute to a retailer’s overall goals? The following sections highlight a few key areas where analytics is increasingly finding its footing in the online retail sector.

SEGMENTATION

For the uninitiated, Segmentation refers to dividing a market (customers) into distinct segments that possess similar attributes and characteristics. The rationale behind doing so is that since the occupants of a particular segment convey similar attitudes, wants and needs their chances of responding to communication that satisfies the above becomes exponentially more. And in the case of online retailing the hordes of analytics generated at every turn make segmentation possible.

Every single scrap of data originating anywhere from a registration form to a payment preference selection method contributes to enriching a segment. Demographic, Psychographic and Behavioral are the three most common categories of segmenting customer bases.

DEMOGRAPHIC, PSYCHOGRAPHIC AND BEHAVIORAL ARE THE THREE MOST COMMON CATEGORIES OF SEGMENTING CUSTOMER BASES.

Each of the above segmentation ‘buckets’ in turn contains various ‘segmentation variables’ which can in turn be defined and quantified by specific customer or web data. Some examples of segmentation variables include

● Search Engine Analytics (Keyword Search)

● In-portal Search Data

● Product/Page wise insights

● Cart Status (Additions, Removals, Abandonments, Checkouts)

● Dispatch preferences (Normal, Same Day, Next Day etc.)

● Similar and higher product uptake propensity

● Promotions responses

● Average order size and value

A simple example is as follows. Online shoppers can be fall into two segments by just looking at their historical cart behavior - Browsers (shoppers who browse products without purchases) versus Buyers (who purchase products more often than not). Once identified, the various interests and preferences of the segment (Buyers) relating to content, product pages, payment and delivery options etc. can be inferred and be leveraged better to maximize revenue. Additionally, for cart abandoners (Browsers), retargeting campaigns can also be run to re-ignite their interest and convert them into buyers.

By understanding the various segments, online retailers can then tailor communication that maximizes high value segments and jump starts the underperforming ones.

The three big segmentation categories

Each of the above segmentation ‘buckets’ in turn contains various ‘segmentation variables’ which can in turn be defined and quantified by specific customer or web data. Some examples of segmentation variables include

● Search Engine Analytics (Keyword Search)

● In-portal Search Data

● Product/Page wise insights

● Cart Status (Additions, Removals, Abandonments, Checkouts)

● Dispatch preferences (Normal, Same Day, Next Day etc.)

● Similar and higher product uptake propensity

● Promotions responses

● Average order size and value

A simple example is as follows. Online shoppers can be fall into two segments by just looking at their historical cart behavior - Browsers (shoppers who browse products without purchases) versus Buyers (who purchase products more often than not). Once identified, the various interests and preferences of the segment (Buyers) relating to content, product pages, payment and delivery options etc. can be inferred and be leveraged better to maximize revenue. Additionally, for cart abandoners (Browsers), retargeting campaigns can also be run to re-ignite their interest and convert them into buyers.

By understanding the various segments, online retailers can then tailor communication that maximizes high value segments and jump starts the underperforming ones.

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‘SEGMENTATION VARIABLES’ CAN IN TURN BE DEFINED AND QUANTIFIED BY SPECIFIC CUSTOMER OR WEB DATA.

Segmentation variables

Each of the above segmentation ‘buckets’ in turn contains various ‘segmentation variables’ which can in turn be defined and quantified by specific customer or web data. Some examples of segmentation variables include

● Search Engine Analytics (Keyword Search)

● In-portal Search Data

● Product/Page wise insights

● Cart Status (Additions, Removals, Abandonments, Checkouts)

● Dispatch preferences (Normal, Same Day, Next Day etc.)

● Similar and higher product uptake propensity

● Promotions responses

● Average order size and value

A simple example is as follows. Online shoppers can be fall into two segments by just looking at their historical cart behavior - Browsers (shoppers who browse products without purchases) versus Buyers (who purchase products more often than not). Once identified, the various interests and preferences of the segment (Buyers) relating to content, product pages, payment and delivery options etc. can be inferred and be leveraged better to maximize revenue. Additionally, for cart abandoners (Browsers), retargeting campaigns can also be run to re-ignite their interest and convert them into buyers.

PERSONALIZATION

A much welcomed facet of online retail is the opportunity to personalize. Personalization has bidirectional benefits for customers as well as retailers. Online shoppers are made to feel special when they receive personalized offers and recommendations while retails utilize the same as an opportunity to learn more about customers with the ultimate aim of maximizing their average revenue per customer. The success of personalization is highlighted in a recent survey conducted by Infosys that revealed close to 86% of online shoppers had reported a marked influence of personalization on their purchase choices. And analytics is vital in making personalization work and to help build relationships within online retail.

Using analytics, online retailers can run personalized promotions and offers campaigns intended to target and resonate with customers accurately. Regardless of whether the promotions are on-site, email, mobile or any medium for that matter analytics are a vital driver to their success. For example, buying habits, long term purchase history and social media activity are excellent customer data generation points. Analyzing the data generated from these sources can provide a clear insight into the customers’ psyches which can then be leveraged and incorporated into future communication to create a positive customer experience.

By understanding the various segments, online retailers can then tailor communication that maximizes high value segments and jump starts the underperforming ones.

PERSONALIZATION HAS BIDIRECTIONAL BENEFITS FOR CUSTOMERS AS WELL AS RETAILERS.

Leverage Analytics

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Each of the above segmentation ‘buckets’ in turn contains various ‘segmentation variables’ which can in turn be defined and quantified by specific customer or web data. Some examples of segmentation variables include

● Search Engine Analytics (Keyword Search)

● In-portal Search Data

● Product/Page wise insights

● Cart Status (Additions, Removals, Abandonments, Checkouts)

● Dispatch preferences (Normal, Same Day, Next Day etc.)

● Similar and higher product uptake propensity

● Promotions responses

● Average order size and value

A simple example is as follows. Online shoppers can be fall into two segments by just looking at their historical cart behavior - Browsers (shoppers who browse products without purchases) versus Buyers (who purchase products more often than not). Once identified, the various interests and preferences of the segment (Buyers) relating to content, product pages, payment and delivery options etc. can be inferred and be leveraged better to maximize revenue. Additionally, for cart abandoners (Browsers), retargeting campaigns can also be run to re-ignite their interest and convert them into buyers.

A real life example of personalized targeting is that of a well-known online retailer of home furnishings and appliances. The retailer utilized a combination of customer behavioral data and email marketing to run a series of successful customer lifecycle campaigns. Customers were tracked in the entirety during their time on the site and were targeted with automated personalized emails at specific behavioral points. For example, customers who completed transactions were sent a thank you email, while cart abandoners were sent discount emails to persuade them to complete their transactions. Additionally, customer information such as birthdays, anniversaries and festive seasons were also used as opportunities to reach out to customers and build a successful relationship. Needless to say the initiative was an unprecedented success and average revenue per email more than doubled.

The above described campaign is just one in a host of ways analytics can help personalization. Other areas where analytics driven personalization is increasingly gaining traction include Product Recommendations, Loyalty Schemes, and CRM to name a few.

By understanding the various segments, online retailers can then tailor communication that maximizes high value segments and jump starts the underperforming ones.

OTHER AREAS WHERE ANALYTICS DRIVEN PERSONALIZATION IS INCREASINGLY GAINING TRACTION INCLUDE PRODUCT RECOMMENDATIONS, LOYALTY SCHEMES, AND CRM TO NAME A FEW.

Each of the above segmentation ‘buckets’ in turn contains various ‘segmentation variables’ which can in turn be defined and quantified by specific customer or web data. Some examples of segmentation variables include

● Search Engine Analytics (Keyword Search)

● In-portal Search Data

● Product/Page wise insights

● Cart Status (Additions, Removals, Abandonments, Checkouts)

● Dispatch preferences (Normal, Same Day, Next Day etc.)

● Similar and higher product uptake propensity

● Promotions responses

● Average order size and value

A simple example is as follows. Online shoppers can be fall into two segments by just looking at their historical cart behavior - Browsers (shoppers who browse products without purchases) versus Buyers (who purchase products more often than not). Once identified, the various interests and preferences of the segment (Buyers) relating to content, product pages, payment and delivery options etc. can be inferred and be leveraged better to maximize revenue. Additionally, for cart abandoners (Browsers), retargeting campaigns can also be run to re-ignite their interest and convert them into buyers.

CUSTOMER SATISFACTION

Retailers that use analytics to strengthen their customer experience and service are said to experience far more favorable results when it comes to customer satisfaction. Studies by The Aberdeen Group suggest that ‘39 percent more satisfied with their abilities to make service decisions driven by data compared to those companies that do not use analytics for service’.

Hence the potential to empower customer satisfaction using a combination of analytics and customer experience is enormous. For example, analytics can help online retails gauge customer sentiments towards a product or group of products and prepare them to deal with positive or negative customer engagement in the best way possible. By studying a variety of customer touchpoints (such as forums, blogs, social networks etc.) after the release of a new product line, retailers can determine the overall customer sentiments (positive or negative) that become associated with the product line. Retailers can then leverage any positive sentiments and deepen customer engagement towards the long term and in the case of negative sentiments, retailers can anticipate any backlash and take a proactive stance on how to manage customer satisfaction.

By understanding the various segments, online retailers can then tailor communication that maximizes high value segments and jump starts the underperforming ones.

ANALYTICS CAN HELP ONLINE RETAILS GAUGE CUSTOMER SENTIMENTS TOWARDS A PRODUCT OR GROUP OF PRODUCTS AND PREPARE THEM TO DEAL WITH POSITIVE OR NEGATIVE CUSTOMER ENGAGEMENT IN THE BEST WAY POSSIBLE.

Delighting through Personalization

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CONCLUDINGTHOUGHTSThe success of online retails is increasingly becoming reliant on the robustness of the internal analytics framework. While the above examples are a window into the world of retail analytics, the scope and applications of analytics in online retail are more far reaching.

Online Retailers must recognize the fact that a data driven strategy is the key to a sustainable market presence and embrace it as an opportunity to change the face of retail forever.

THE SCOPE AND APPLICATIONS OF ANALYTICS IN ONLINE RETAIL ARE MORE FAR REACHING.

Each of the above segmentation ‘buckets’ in turn contains various ‘segmentation variables’ which can in turn be defined and quantified by specific customer or web data. Some examples of segmentation variables include

● Search Engine Analytics (Keyword Search)

● In-portal Search Data

● Product/Page wise insights

● Cart Status (Additions, Removals, Abandonments, Checkouts)

● Dispatch preferences (Normal, Same Day, Next Day etc.)

● Similar and higher product uptake propensity

● Promotions responses

● Average order size and value

A simple example is as follows. Online shoppers can be fall into two segments by just looking at their historical cart behavior - Browsers (shoppers who browse products without purchases) versus Buyers (who purchase products more often than not). Once identified, the various interests and preferences of the segment (Buyers) relating to content, product pages, payment and delivery options etc. can be inferred and be leveraged better to maximize revenue. Additionally, for cart abandoners (Browsers), retargeting campaigns can also be run to re-ignite their interest and convert them into buyers.

By understanding the various segments, online retailers can then tailor communication that maximizes high value segments and jump starts the underperforming ones.

Retail gets an all new face

RETAIL

ABOUT XERAGO05.................................................

Xerago is a new age marketing solutions company with a footprint across Asia Pacific, now making its foray into USA. Clients include Citi, DBS, HDFC Bank, SM Retail, Celcom, Starhub, Intel, BharatMatrimony, and a number of other market-leading and start-up brands.

To learn more about Customer Value Maximization and how it can help you,contact your nearest Xerago office.

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