business strategy: socialytics — enabling the social

June 2012, IDC Retail Insights #GRI235658

IDC Retail Insights: Retail Merchandise Strategies: Business Strategy

Business Strategy: Socialyt ics —Enabling the Social Business of Retai l

I D C R e t a i l I n s i g h t s : R e t a i l M e r c h a n d i s e S t r a t e g i e s

BUSINESS STRATEGY #GRI235658

Greg Gi rard Er ic Newmark

I D C R E T A I L I N S I G H T S O P I N I O N

A social media analytics strategy should be developed as an enterprisewide framework even as a socialytics (as coined by IDC) capability is incubated and developed within digital marketing, public relations, or ecommerce — its usual points of origin. The effort should start small and will make investment sense as the vision of socialytics'strategic scope comes into focus. In addition:

● Socialytics can inform decisions beyond marketing in merchandising, commerce, and fulfillment. Actionable insights from socialytics include marketing campaigns; product design and quality; new product development and introduction; assortment localization; pricing, promotions, and personalization; crisis management, selling techniques, and store associate management; store operations and design; and competitive intelligence and differentiation.

● Better decisions from socialytics depend on how well its insight is incorporated within each process' decision management framework and the company's enterprise retail intelligence framework. Success also depends on the business acumen of socialytics analysts as well as their roles, relationships, and decision rights and how each socialytics technology fits its users' needs and its insights complement those of other analytic disciplines.

● Useful insight from socialytics depends on capabilities for harvesting, organizing, and analyzing structured and unstructured data from varied sources in volumes and at velocities unlike those of enterprise data, syndicated data, and so forth common today.

● Socialytics is fairly new; new capabilities are emerging quickly within and around it (e.g., machine learning and Web relevance engines). The forefront of socialytics capabilities will evolve quickly; evermore nettlesome use cases for socialytics will evolve apace.

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#GRI235658 ©2012 IDC Retail Insights

T A B L E O F C O N T E N T S

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In This Study 1

Situat ion Overview 1

The Approach 2

Case Studies — Laboratories of Discovery .............................................................................................. 4

Future Out look 8

Enterprise Socialytics Enables the Social Business of Retail ................................................................... 8

Socialytics Capabilities in the Making ....................................................................................................... 16

Essent ial Guidance 17

Actions to Consider................................................................................................................................... 17

Learn More 20

Related Research..................................................................................................................................... 20

©2012 IDC Retail Insights #GRI235658

L I S T O F T A B L E S

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1 Research Project Milestones........................................................................................................ 3

2 Socialytics Applications in Retail .................................................................................................. 12

3 Key Methodological Aspects of Socialytics in Retail .................................................................... 15


L I S T O F F I G U R E S

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1 IDC Retail Insights' Omnichannel Orchestration and Optimization Model.................................... 9

2 Retail Enterprise Socialytics Model .............................................................................................. 11

3 IDC Typology of Socialytics Methodologies ................................................................................. 13

©2012 IDC Retail Insights #GRI235658 Page 1

I N T H I S S T U D Y

A growing and diverse number of consumers now interconnect and influence one another with their use of social networking and media —YouTube, Facebook, Twitter, LinkedIn, Google+, blogs, wikis, and ratings and reviews. The speed, scale, and viral aspects of social media and networking influences, be they facts, opinions, or misinformation, dwarf old modalities of "word of mouth" and "power of the pen"pressures on shaping consumers' attitudes and opinions about brands and their buying patterns and propensities. Another complication is that conventional ways by which retailers seek to understand consumer sentiments and the sources of influence on those sentiments fall short of the mark when their customers engage one another through social media and networks. The disparity between the speed and strength of consumer sentiments spreading across social networks and the limits of what retailers have to track these sentiments put retailers at the risk of losing control of their brand, the loyalty of their best customers, and market share, revenue, and margins. The question for retailers is deciding how they should ready themselves to understand, react, and ideally engage their consumers in this brave new world.

Based on an in-depth six-month research program, this report identifies best practices and opportunities for applying social media analytics, coined socialytics by IDC, across all core retail processes.

S I T U A T I O N O V E R V I E W

To provide retailers with a detailed illustration of how they can best harness social media analytics (coined socialytics by IDC) within their business, IDC Retail Insights conducted a six-month research study in which two United States–based broadline retailers and one fast fashion youth retailer were matched up with two leading socialytics software vendors to carry out six independent two-month long proof-of-concept projects.

Twenty-eight software vendors offering socialytics were initially surveyed about their software capabilities, strategic focus and, most importantly, experience in delivery value through socialytics to the retail industry. This process allowed IDC Retail Insights to develop a short list of four vendors; ultimately, two vendors cited resource constraints and withdrew before we made the final selection. Two primary vendors with strong retail experience were ultimately invited to participate in this study.


The project was designed to focus on process, illustrate best practices, and provide lessons learned around how retailers can best engage with socialytics vendors. In that, the purpose of our research was not to evaluate any vendor; we have not identified the short-listed vendors or the two vendors that participated in the research.

The 28 vendors consider for this study are Attensity, Adobe, Brandwatch, Buzzcapture, Buzzient, Clarabridge, Converseon, Crimson Hexagon, Cymfony (recently acquired by Visible Technologies), IBM, Jive Software, KANA, Lexalytics, Lithium, Meltwater, MicroStrategy, NetMiner, NetBase/SAP, Oracle, Radian6, SAS, Spredfast, Sysomos, Teradata, Twelvefold Media, Visible Technologies, and Webtrends.

The context and takeaways from each project are fully detailed in the Case Studies: Laboratories of Discovery section. Further, the best practices resulting from these projects, combined with information acquired from prior research, led to the creation of IDC Retail Insights'Retail Enterprise Socialytics model, which is described in the Future Outlook section of this report.

THE APPROACH

The total allotted time for the proof-of-concept phase of the research project was two months, which required strict time management by all parties. All six parties were invited to participate in this project during February 2012, and each of the three retailers was formally introduced to both vendors at the beginning of March 2012. Prior to the introduction, IDC Retail Insights had discussed the project in detail with each retailer independently and collectively and had achieved agreement among the group that they would each choose a more-or-less common, narrow area of focus for the application of socialytics. The need for a common, narrow area of focus was dictated by practicalities of the project and the resources all parties could devote to the effort.

After all parties were introduced, each retailer held separate private conversations individually with each vendor to further refine its project scope and agree on search terms, taxonomies, and relevant Web sites of interest. Once the scope of each project was solidified, the vendors then had roughly one week to prepare for the listening phase of the project, in which they would begin collecting data from the social Web (focusing on agreed upon Web sites and sources of information via the mutually defined search terms and taxonomy). The listening period was scheduled to begin on March 15, with an open-end date, only defined by the need for each vendor to present its findings to each retailer on April 15.


Table 1 presents the key milestones before, during, and after the March 15 to April 15 field period.

T A B L E 1

R e s e a r c h P r o j e c t M i l e s t o n e s

Milestone Event, Deliverable, and/or Activity

1 Final confirmation of three retailers' participation. All parties sign NDA documents.

2 IDC Retail Insights hosts kick-off conference call for participating retailers. IDC Retail Insights

summarizes its body of research on social media analytics in retail. Retailers clarify their objectives for

the project.

3 IDC Retail Insights documents retailers' research interests — information about social media analytics

themselves and the insights they want social media analytics to provide.

4 IDC Retail Insights delivers terms of reference governing vendors' efforts in this project with timelines

and milestones.

5 Kick-off conference call is hosted by IDC Retail Insights for all parties — retailers and vendors.

6 Each vendor executes three separate three- to four-week social media analytics campaigns focused on

each of the retailers.

6.1 Vendors stream analytics to each retailer as well as a confidential copy to IDC Retail Insights.

6.2 IDC Retail Insights interviews each retailer privately weekly to discuss its experience and what it learned

and identify best practices.

6.3 IDC Retail Insights reviews vendors' progress weekly.

7 IDC Retail Insights summarizes findings related to vendors' performance and hosts conference call to

present them to all retailers.

8 IDC Retail Insights hosts conference calls — one-to-one vendor presentation to each retailer.

9 IDC Retail Insights hosts capstone session with retailers (conference call or in-person meeting).

10 IDC Retail Insights summarizes the final findings for publication, subject to retailers' review for

confidentiality. (Retailers are presented complimentary copies of the report.)

Source: IDC Retail Insights, 2012

The outcome of each project is described in three case studies discussed in the sections that follow. It should be noted that to maintain the timeline, hit all milestones, and conclude within the two-month window, both vendors and retailers alike were under pressure to perform quickly and were juggling each POC alongside several other internal projects, which should be taken into account when analyzing the results.


The case studies have been written from the retailer's perspective, combining commentary on each retailer's two respective projects to help compare and contrast different approaches, challenges, and lessons learned. As needed a few times to distinguish one vendor from another in this report, the two vendors have been identified as vendorA and vendor B.

C A S E S T U D I E S — LABORATORIES OF D ISCOVERY

C a s e 1 : R e t a i l e r A — A B i g - B o x R e t a i l e r

Background

Retailer A, a leading United States–based broadline retailer, already regularly performed its fair share of social media analysis when IDC Retail Insights approached it for this project. But coincidentally, the retailer had been performing an internal review on the value of social media analysis to see what type of ROI it was getting in comparison with its display analytics and search analytics efforts. As a result, the timing of this project was very beneficial because it allowed the retailer to dig deeper into internal discussions around areas for potential expansion and improvement in its socialytics initiatives. The project also provided the retailer a firsthand look at two leading vendors in the space, should it ever choose to work with them in the future.

The retailer had noticed a recent uptick in sales around one of its particular clothing categories, so focusing this project on the social discussions occurring in relation to that topic and trying to derive greater insight into what was driving it became a logical focus for the project.

The Takeaways

While the combined analysis delivered to the retailer from both vendors provided some interesting insights, the volume of brand-new insight gained was limited because the retailer already conducts socialytics on a regular basis. Considering the project only incorporated two weeks of analysis and the retailer already conducts socialytics regularly, the project was largely expected to be more of a confirmation exercise than one uncovering earth-shattering insights. This proved to be the case.

The project served as a very insightful "sandbox," helping identify opportunities for better collaboration across the organization and understand how to better manage the social media analysis process with the different decision management framework across the organization.


From a process perspective, it should be noted that the level of effortexceeded the retailer's expectations because of frequent (weekly) calls and/or meetings with one of the vendors and the effort to develop search terms and the like to kick off the project. The latter concern —the level of effort to produce search terms for this narrowly scoped effort — highlighted the importance of master data management and data governance for running socialytics at scale. Furthermore, this concern and the concern of Retailer B regarding the management of business term synonyms suggest the importance of introducing machine-learning capabilities in next-generation quantitative natural language processing (NLP) engines.

Methodologically, the pilot demonstrated the value of leveraging another vendor's voice of customer data, which the retailer already had on hand, to complement the social content the vendors harvested themselves.

The pilot also demonstrated to Retailer A that social content harvestedand maintained from a period of time prior to the listening period did not in this particular instance add a lot of value. We concluded that the value of applying preexisting socialytics metrics depends on its having been harvested and analyzed in a manner aligned with the objectives of the current investigation.

Several meaningful insights about socialytics process and vendor engagement best practices were gained by the retailer through participation in this process. According to the retailer, the three biggest lessons learned from the project are:

● Realizing the importance of including a broadly diversified team of stakeholders (This project was led by digital marketing, with the customer insight analytics team only consulted during the late innings of the project. It should also be noted there was no involvement from the merchandising department, which was identified as an omission in hindsight. Bringing these departments in from the beginning would have led to more well-defined listening goals, topics, and search terms — all aligned with a broader set of business objectives.)

● Understanding what to expect if the retailers work with a third-party socialytics vendor in the future to supplement their own internal work (This includes what questions will be asked of them, how much time and effort it will necessitate, and what information and data sources will be required to gain deep meaningful insight.)

● Complementing the second bullet, understanding that a tightlyknitted approach that weaves the science of socialytics with the needs of the business throughout each analytical campaign will increase the quality and utility of insights gained


The retailer also realized that to take socialytics to the next level, it needs to focus heavily on improving data integration across the enterprise, with a special focus on linking social media analytic decision frameworks to transaction systems. The ability to tie social analysis to transactional behavior at each individual store was shown to be vital to truly measuring social impact and marketing's ability to convert social behavior into revenue.

C a s e 2 : R e t a i l e r B — A B i g - B o x R e t a i l e r

Background

Retailer B, a leading United States–based big-box retailer, came to this project with a strong evaluative focus on data accuracy. This retailer asked the vendors to listen to customer chatter online regarding assortment, availability, and pricing in relation to promotions aligned with two seasonal events. The vendors were given a wide berth to"learn something new" and focus their deliverable on whatever findings they thought were most insightful. The retailer provided both vendors with a complete list of its Facebook pages, company-owned Web sites for its brands, and its Twitter handles.

The Takeaways

This pilot surfaced the importance of driving an alert-into-workflow process such that comments like "they will lose my business if they don't contact me" are routed to the CRM organization to proactively address customer problems and concerns — confirming the same finding in the first case study. While the volume of such red flag warnings did not warrant it during this pilot, the study team agreed that at high volumes, the alerting process needs to have filtering and routing capabilities.

This pilot also demonstrated the value of well-designed word clouds for insight into customer emotions (i.e., love, obsession, and hate) and behaviors (i.e., buy, want, and return), customer-ascribed product attributes (i.e., fit, service, and cheap), and themes and life activities(i.e., school, sports, and dining).

This pilot also brought out the value of distinguishing between passion and sentiment as well as the value of drill-down capabilities from summary representations to the underlying verbatim text and of the value of filtering and weighting comments (e.g., the sources' level of influence).


A review of some of the findings led to a concern that one or another of the vendor's listening queries, typically Boolean logical expressions, were not well constructed. This was attributable to the vendors not being familiar with the business, not at all surprising in the context of a pilot. Positively stated beyond the limits of a pilot, this demonstratesthe importance of a socialytics analyst having an intimate understanding of the ongoing, and quickly changing, topical concernsof the business. Taken further, this implies that socialytics tools should be business-user friendly (which is not to say these vendors' tools weren't user friendly) and need to be used by the business users within their ongoing business processes and decision-making frameworks —a set of technology and business change management challenges.

Questions about the quality of the listening queries led to anotherconcern that "unrelated noise" might not have been filtered out (e.g.,stock market discussion), while the impact of some relevant business events and market conditions might have been missed. These considerations illustrated the need for a balancing act — the ability to recognize and include topics of adjacent and relevant interest not explicitly identified in the search queries complemented by the ability to disregard adjacent but irrelevant topics.

The criticality of search term semantics also came to the fore, in this case the need to understand business term synonyms (e.g., "loyalty card," "reward program," and "Retailer B card," all meaning the same thing).

Overall, the study team concluded that this case study evidenced the need for close coordination between analysts, whether the vendor's resources, as was the case in this pilot, or a retailer's own socialytics analysts in a production setting and decision makers in a business process. Three characteristics of superior coordination were identified: frequent interactions, specificity and clarity in the dialectic of business problems and analytical findings, and the vendor/analyst's knowledgeof market conditions and business concerns to which socialytics is applied.

C a s e 3 : R e t a i l e r C — A F a s t F a s h i o n

C l o t h i n g R e t a i l e r

Background

Retailer C, a leading fast fashion youth retailer, began this project focused on customer sentiment toward a just-launched trendy clothing line, the related promotional campaign, and customer sentiment toward a few key competitors and their competing merchandise. The analysis was shifted just as the listening period of the project kicked off, and its focus was trained on tactical insights that could inform changes in operations store by store to sell more clothes. Both vendors did their best to accommodate the change.


The Takeaways

As happens most times when the scope of a project is abruptly changed, the application of socialytics in this pilot proved least usefulcompared with the other two pilots.

The vendors' efforts did demonstrate the ability of socialytics to provide insights into customers' perceptions of stores and the impact of their sentiments and impressions on sales. For example, a graph plotting an individual store's structured Yelp score, based on the customers' unfavorable to favorable ratings (on a scale of 1–5), against the vendor's sentiment score of each customer's verbatim comments proved insightful. Circles of varied sizes indicated the number of Yelp ratings for each store at its point on the graph. Customer verbatims for each store added specifics — the good, the bad, and the ugly valuable to a store manager.

The analytic point of note here is that the customers' self-reported Yelp scores did not correlate with their sentiment as revealed by socialytics'natural language processing. While it couldn't be said for certain from the study team's perspective since the stores organization was not part of the team, it appeared that socialytics yielded actionable insight,which Yelp scores alone could not.

FUTURE OUTLOOK

E n t e r p r i s e S o c i a l y t i c s E n a b l e s t h e S o c i a l

B u s i n e s s o f R e t a i l

O3 Retail Enterprise Technology Model — The Context

for Socialytics

Managing and observing the six parallel proof-of-concept projects led us to conclude that the design, development, and deployment of socialytics should be seen from the outset as an enterprisewide undertaking even as socialytics capabilities incubate within marketing and typically in digital marketing, or in ecommerce. Discussions with all three retailers pointed to the conclusion that socialytics can inform a much wider scope of decisions, extending at least into customer insight, public relations, merchandising, product development, store operations, including workforce management, call center management, promotions and pricing, and associate training. In addition, some unstructured data generated within the enterprise (e.g., ratings and reviews, online chats, and store associates' "voice of customer"commentaries) are fodder for the techniques of socialytics.


Given the varied roles socialytics can plan across the enterprise, we drew the conclusion that this discipline needs to be situated technologically and organizationally within fundamental retail processes and their retail intelligence platforms. The O3 model of Omnichannel Orchestration and Optimization is useful in aligningsocialytics in these regards across the retail enterprise (see Figure 1).

The O3 model envisions analytics that can handle the volume, variety, and variations of big data amassing in the digital universe, mobility that creates "online inside" experiences bursting channel and process boundaries, social business that enriches decision environments with collective intelligence and enables process streams with "follow" and "like" switches, and the cloud, now extended to an application services bus.

O3 derives its name from capabilities inherent in the environment just described — integrated business intelligence, automated processes, and engagement applications that can enable and, to some extent,automate next best action processes. These processes are manifested in four omnichannel domains — merchandising and marketing on the one hand and fulfillment and commerce on the other. These domains are girded by product and customer life-cycle management on a foundation of process and content management. Next best action processes are informed by four types of retail intelligence —merchandise, channel, customer, and offer.


F I G U R E 1

I D C R e t a i l I n s i g h t s ' O m n i c h a n n e l O r c h e s t r a t i o n a n d

O p t i m i z a t i o n M o d e l


Socialytics Enterprise Design Objectives

Organizational, process, and technology design objectives in creating an enterprise retail socialytics capabilities should include:

● Creating an enterprisewide approach under the governance of a leadership team drawn from across four core omnichannel processes: marketing, commerce, merchandising, and fulfillment

● Informing the four aspects of retail intelligence with insights drawn from the social universe: customer intelligence, offer intelligence, channel intelligence, and merchandise intelligence


● Delivering insights within the decision management context of the roles they inform — that is, task aligned and just-in-time and in the style and at the speed by which these decisions are taken

Socialytics — A Retail Enterprise Model

Figure 2 presents a high-level view of the span of socialytics methodologies — data generation, harvesting, analyzing, and measuring — across the retail enterprise and the organizational dimensions where its insights can inform decisions.

This retail enterprise socialytics model is the framework for:

● Aligning social media analytics to the insight needs of fulfillment, commerce, merchandising, and marketing processes with brand strategy and presenting these insights within each process' decision management framework

● Designing four core socialytics capabilities — harvesting data,organizing it, analyzing it, and measuring and reporting insights to meet needs of core processes

● Understanding sentiments, directionality, virality, influence, and other measures of signals about a retail brand's position in the digital universe

● Incorporating social media insights into a common retail intelligence repository

● Providing the analytical foundation to enable next best actions across the enterprise based on such understanding


F I G U R E 2

R e t a i l E n t e r p r i s e S o c i a l y t i c s M o d e l


Business Process and Organizational Considerations

A well-constructed social media analytics strategy spans all retailing activities that generate social media content. Within each of thesedomains, based on the methodology described in Methodological Aspect of Retail Socialytics section, there are socialytics applications holding significant potential financial, customer, and brand value, for example, including those presented in Table 2.


T A B L E 2

S o c i a l y t i c s A p p l i c a t i o n s i n R e t a i l

Process Process Responsibilities Exemplary Applications and Use Cases

Brand

management

and marketing

Brand differentiation

Support and execution of brand

management differentiation

strategy via development and

execution of media campaigns

Customer analytics, insights,

and segmentation

Creation of promotional

campaigns to shape customer

demand in collaboration with

merchandising

Identification and characterization of threats to brand values

(e.g., boycotts or campaigns reacting to unmet corporate social

responsibility commitments)

Intelligence about strengths and weaknesses of competitors for

customer segments, merchandise categories, product quality,

pricing, locations, and so forth of strategic importance

Intelligence about customer sentiment trends, by customer

segments for merchandise categories, product quality, pricing,

locations, and so forth of strategic importance

Identification of networks, nodes, and personalities of viral

influence

Assessment of the impact of marketing campaigns and events

on consumer awareness, sentiments, behaviors, and intentions

Merchandising Selection, curation, localization,

allocation, distribution, and

pricing of merchandise in all

commerce channels

Creation of promotional

campaigns to shape customer

demand in collaboration with

marketing

Intelligence about new product development, design, and

introduction — aspects that delight customers, meet their

expectations, or are of low value

Intelligence about local demand for products and services not

carried in local assortment

Fulfillment Sourcing, developing,

designing, and delivering of

merchandise into commerce

channels in collaboration with

merchandising

Fulfilling customer orders from

own network, suppliers, and

marketplace

Early warning of product defects and shortfalls of performance

and attributes against customer expectations

Intelligence about new product development, design, and

introduction — aspects that delight customers, meet their

expectations, or are of low value

Commerce Presenting and selling

merchandise and services

across all channels

Operating all channels of trade

including stores, catalog, call

centers, and digital — mobile,

ecommerce, social, and third-

party channels

Store- and market-specific customer concerns and delights

regarding store operations, associates engagement practices

and product knowledge, customer service, crowds, wait times,

and so forth

Real-time in-store, near-store customer tweets for customer

service, product information, and product location

Customer concerns and delights about performance and

characteristic of ecommerce, mobile, social, and other digital

channels



Methodological Aspect of Retail Socialytics

IDC Typology of Socialytics Methodologies

Figure 3 depicts the IDC typology of socialytics methodologies, from foundational capabilities of text analytics on the left through three analytics that successively enable the next — from social data analytics to social relationship analytics and social collaboration analytics.

F I G U R E 3

I D C T y p o l o g y o f S o c i a l y t i c s M e t h o d o l o g i e s

Source: IDC, 2011

Progressing left to right each provides increasingly sophisticated insight into concerns such as tone and intensity of sentiments, topic trending, influence of individuals, and the strength of communities. The data objects of each type's analysis accumulate from the agnostic approach of text analytics through three data types that represent increasingly critical social forces of enterprise concern — matters that impact, negatively or positively, a retailer's social capital.

Methodological Enablers — Harvest, Organize, Analyze, and Report

Our research illuminated the importance of four methodological enabling capabilities characteristic of an enterprise approachembedded in the platforms for the application of socialytics in retaildecision management frameworks:


● Harvest: Technologies to acquire content from internal sources such as customer emails, call logs, online chat transcripts, and employee "voice of customer" programs as well as content fromthe digital universe of 3I consumers — instrumented, informed, and interconnected beyond the enterprise:

○ Sites and campaigns the retailer owns, manages, or primarily influences (e.g., a Facebook fan page)

○ Independent social media properties where most semantic and structured social activities related to the retailer's processes, and to those of its competitors, are created, streamed, and maintained

○ Sites and campaigns owned and managed by the retailer's competitors (e.g., their Facebook fan pages, product rankings,and reviews on their ecommerce sites)

● Organize: Retail-, brand-, merchandise-, and marketing-tuned semantic taxonomies (or gnomes) to align listening and interpretation to business initiatives — organizing capabilities need to be dynamic with underlying data management and governance to keep pace with business operations, tactics, and strategies (e.g., promotions, marketing campaigns, new product introduction, and store changeovers)

● Analyze: Natural language processing, semantic analysis, influential vector metrics, and other analytics:

○ Unstructured text-based semantic actions (e.g., I like the color of those shoes and would really love them if they fit better) —the object of NLP and semantic analysis

○ Structured social actions (e.g., "Like" something on Facebook) — the object of quantitative analytics

○ Measurement of the directionality and virality of these actions within a social context — the actor's social graph — the object of vector analysis

● Measure and report: KPIs to inform business decisions, in particular, business decisions that social media insight can improve, and alerts to spot events and trigger next best actions

These capabilities are summarized in Table 3 in their runtime process sequence, starting with harvesting and ending with reporting. Note that these capabilities need to be tackled in reverse order during design and configuration — starting with measuring and reporting within decision management frameworks and then through the rest.


T A B L E 3

K e y M e t h o d o l o g i c a l A s p e c t s o f S o c i a l y t i c s i n R e t a i l

Methodological

Dimension Function Core Capabilities and Critical Considerations

Harvest Acquires social media

content and internal

content (e.g., call logs) for

analysis

Technologies to acquire data and content from social media

sources

Governance process for retaining historical data

Organize Aligns harvesting and

analyzing dimensions to

business initiatives

Taxonomies, gnomes, or lexicons of search terms

A life-cycle process to discover, maintain, and retire the current

set of Web sites, discussion boards, communities, and other

venues where relevant content can be found

A life-cycle process to maintain a comprehensive library of search

terms tagged for topics and constituencies for whom they are

maintained

Alignment of data sources to areas of concern (e.g., Yelp, Yahoo!

Local, Urbanspoon, CitySearch, and Google Local for market-level

data about stores, trends, and hot topics)

Constant turning and dynamic reconfiguration of data models to

the needs of strategy, brand, merchandise, and marketing

decision management

Master data management governance

Analyze Extracts relevant signals

about matters of concern

and converts them into

domain intelligence

Quantitative natural language processing and related disciplines

Nodes of influence and vector metrics

Multidimensional segmentation

Scale, scope, and speed

Premium on analysts' business acumen

Self-evident presentation and the use of analytical models and

means

Predictive analytics

Report Converts socialytics signals

into KPIs to inform decision

making

Is incorporated into

decision-making

frameworks and processes

including, for example,

alerts of disruptive events

Process, role, and task alignment

Workflow enablement

Business intelligence foundation



S o c i a l y t i c s C a p a b i l i t i e s i n t h e M a k i n g

Evolving Capabilities

While there are some self-learning capabilities in socialytics tools today, the development and maintenance of socialytical content (e.g., search terms, product attributes, properties, and Web sites and its models) are largely a manual process. We expect that the application of machine-learning techniques will soon automate some and eventually the majority of these efforts.

For example, today a socialytics analyst assigned to understand the impact of a marketing campaign on customer perceptions needs to enumerate search terms and Web sites related to the particular campaign. In a machine-learning approach, the analyst would seed the model with a small set of search terms and semantic algorithms would then create a broader range of search terms in an iterative process as it rapidly and continuously "tests and learns" each term's relevance —adding more relevant ones and deleting less relevant ones.

Nearer term, we expect that natural language processing will automate the maintenance of product attributes associated with products and categories (i.e., drawing them from unstructured product description text, rather than through manual enumeration as required today).

Finally, after one or two recent announcements about these capabilities, we expect to see more socialytics applications emerge that incorporate workflow and alerts, with analytical filters, dashboards, and prioritization logic, connected to decisions and actions these dependent decisions trigger.

Evolving Use Cases and Competing or

Complementary Analytics

Socialytics will need to keep pace with developments shaping the evolution of the retail digital universe of interconnected, informed, and instrumented shoppers and enabling competitive tactics. These include the sort of dynamic algorithmic Web page creation technologies we see emerging today — technologies that understand the intent of search queries and dynamically assemble Web pages optimized for each search (or each cluster of like queries).

So-called Web relevance engines are emerging to address the mismatch between the slow, low-volume manual editing of Web sites and the rapid, high-volume changes in product availability, inventory, and expressed customer demand. Emerging capabilities can make hitherto hidden but search-relevant content discoverable through search.


New socialytics use cases will be driven by the adoption of in-store WiFi tracking technologies, which can keep tabs in real time on consumers' "online inside scan and scram" activities — the use of smartphones to check prices and products online from store aisle. In these circumstances, socialytics can inform digital and in-person preemption techniques (e.g., online presentation of competitive offers and directing store associates to assist these customers as they consider their options).

Deep analytical modeling capabilities applied to the union of big data (e.g., social graphs and Facebook posts) and traditional transactional data (e.g., POS and offer redemption) will create new consumer behavior models with the ability to dynamically compose product descriptions and promotional offers incorporating first order attributes about the product (e.g., "the dress is sequin blue…") and second order attributes about the product but not found in the product description maintained by the retailer, relevant to a consumer's interests (e.g., continuing the description with "…perfect for a night in Las Vegas" to a shopper planning a trip to Las Vegas).

These and other developments we expect to see will quickly create a much more dynamic semantic landscape. Socialytics technologies will have to evolve faster, more sophisticated self-correcting analytics and models in order to provide timely guidance.

E S S E N T I A L G U I D A N C E

A c t i o n s t o C o n s i d e r

Many of the comments the three retailers made throughout the research process — before, during, and after the proof-of-concept projects — presented a good set of lessons learned. These and our own observations confirmed by retailers and vendors alike are instructive and should be taken into consideration by retailers as they begin or continue their social media analytics programs.

Organizational and Change Management Guidance

A social media analytics program needs to pull a diverse set of stakeholders together. Some organizations are considering a new role, that of social media strategists (with analytics support) to align these groups and coordinate the process of defining enterprise social media objectives. While such diversity is critical to developing a holistic enterprise approach to social media analytics, take care that such an approach does not impede developing concrete short-term and long-term applications.


While social media analytics can provide actionable insight across the enterprise, for example, in product quality, localization of assortments, and store operations, the sponsoring organization of social media analytics, likely marketing or customer insight, needs to have "its house in order" — to some extent — before extending social media analytics further across the enterprise. The extent to which this is necessary depends on the corporate culture and its appetite for innovation.

Applying existing analytics expertise within marketing (e.g., direct mail and media analytics and optimization) to social media analytics will require the adoption of new skills. The so-called digital natives, millennials who've come of age within the digital universe, may be a good option here — provided that the organizational culture can accept them or, more challengingly, because it can't.

Distinguish between the needs of different constituencies when designing the social media analytics for them and the process for delivering their metrics. One-to-one customer use cases (e.g., customer service call centers and outbound communications through Twitter or email) will make better use of filtered atomic social media content with, for example, direct access to customer's Facebook accounts. On the other hand, merchandising and product development will make better use of aggregate social media metrics.

It is important to conduct social media analysis in an intelligent cadence coordinated with all activities that drive blogs, Facebook posting, tweets, and the like. Such activities will stem from the four processes of harvesting, organizing, analyzing, and measuring (refer back to Figure 2). When surges in positive or negative commentary flare up, without understanding how concurrent marketing campaigns, new product introductions, and so forth might be generating a related causal effect, companies can easily misinterpret the underlying reason behind sentiment shifts.

Competitive analysis should be a significant component of your socialytics strategy. Scouring your competitor's Web site, and contrasting your own brand against your competitor's brand across multiple social communities, is among the easiest value-adds that socialytics can provide your business. Create a process to report timely competitive intelligence relevant to key stakeholders' concerns in formats well suited to their decision rights and decision-making styles.

Analytics Guidance

The process of designing socialytics begins with identifying the outcomes and KPIs to be managed with the addition of socialytics insights and then moves onto specifying decisions inherent in managing those KPIs that socialytics insights can inform. The design process then continues into designing analytics, organizing frameworks, and harvesting strategies.


Develop your strategic framework for applying socialytics insights to business concerns. At a high level, these separate into three areas: personalization of customer touch points and engagement; insight into merchandising, marketing, commerce, and fulfillment; and social engagement. Develop use cases within this framework for incorporating socialytics into your channel, customer, offer, and merchandise intelligence strategies.

Developing social media KPIs will take effort. Think through how your use of social media analytics will relate to your use of display analytics and search analytics in the development of social media KPIs.

Don't expect to easily connect social media KPIs to transactional KPIs. Tight integration between transactional data across all channels with socialytics analysis depends on your having solid 360-degree customer analytics. Socialytics complements these capabilities, not replace them.

Don't let disconnects between social media analytics and traditional transaction metrics dilute your organizational commitment to the discipline. Social media analytics will benefit other stakeholders contributing to your company's success — store operations, product development, customer service, returns, and so forth.

Social analysis should always be compared against a historical baseline prior to deriving meaningful conclusions. If sentiment scores around a particular topic spike by 100%, that may be more or less meaningful than one might presume, especially if it historically spikes 1,000% during a similarly run campaign.

When a topic is first broadly tweeted, auto-bots pick it up and create significant additional tweeting noise, which can become the majority of data in a particular topic. Topics might seem exponentially bigger and more important than they are in reality. It is important to ensure the use of listening tools that identify bots and filter out illegitimate sources of online chatter.

Topic virality can be measured by how many nodes down the network it travels (e.g., re-tweets). Make sure your social listening tools can accurately measure this level of impact surrounding data, as well as appropriately weighed data by the level of impact within subsequent analysis.

Using more than one vendor to perform the "listening" part of social media analytics can prove very beneficial in helping retailers contrast and further validate findings to weed out false conclusions.


Selection of search terms needs to be as exhaustive as possible within the area of interest but also limited to it. For example, applying socialytics to a single campaign to promote a small assortment of shoes in one pilot required 25 search terms for categories, 48 for brands, and 8 for competitors to capture fashion trends.

Capturing the right set of search terms may require contributions from several stakeholders (e.g., merchants, marketing, and agencies).

Don't overlook search term management within and across projects. Setting up a cross-functional process to keep search terms in sync with quickly changing areas of focus (e.g., as marketing campaigns change)is critical. Enabling technologies for search term life-cycle and repository management likely won't be provided by a social media analytics vendor.

LEARN MORE

R e l a t e d R e s e a r c h

● Perspective: Next Generation Retail Summit Explores Omnichannel Stores and Social Media Analytics (IDC Retail Insights #GRI234916, May 2012)

● Perspective: Now That Showrooming Has Wall Street's Attention, It's Time for the Omnichannel Store (IDC Retail Insights #GRI234981, May 2012)

● Merchandising Strategies and Retail Analytics Top 10 2012 Trends(IDC Retail Insights #GRI233829, March 2012)

● Worldwide Retail Industry 2012 Top 10 Predictions (IDC Retail Insights #GRI232576, January 2012)

S y n o p s i s

This IDC Retail Insights report identifies best practices and opportunities for applying social media analytics, coined socialytics by IDC, across all core retail processes. Social retailers that understand consumers' interests, intentions, and influence within the digital universe have the tools to make better decision than those made by their competitors still running blind to such matters. While often incubated in digital marketing or ecommerce, realizing the full potential of socialytics depends on an enterprise approach. Such an approach, managed within the framework of IDC Retail Insights'Retail Enterprise Socialytics model, empowers merchants, store and ecommerce operators, product developers, and marketers as social retailers.


"The disparity between the speed and strength of consumer sentiments spreading across social networks and the limits of what retailers have to analyze these sentiments put retailers at the risk of losing control of their brand, the loyalty of their best customers, and market share, revenue, and margins and of missing the opportunity to gain insight for better decision making and outcomes. Social retailers — those that harness insights from analyzing what their customers and other consumers are saying about them and their competitors in the digital universe — have the tools to make better decisions than their competitors. We found use cases in marketing, customer service, merchandising, product development, store operations, ecommerce, store operations, and customer order fulfillment where bringing socialytics insights to bear can lead to better outcomes," says Greg Girard, program director, Merchandising Strategies and Retail Analytics.

C o p y r i g h t N o t i c e

Copyright 2012 IDC Retail Insights. Reproduction without written permission is completely forbidden. External Publication of IDC Retail Insights Information and Data: Any IDC Retail Insights information that is to be used in advertising, press releases, or promotional materials requires prior written approval from the appropriate IDC Retail Insights Vice President. A draft of the proposed document should accompany any such request. IDC Retail Insights reserves the right to deny approval of external usage for any reason.

business strategy: socialytics — enabling the social

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