The New Imperative The Role of Data and Analytics in B2B ...

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  • “Analytics” definition and its drivers What’s important to B2B marketers? How does analytics impact B2B marketing challenges?
  • This is perhaps more true for marketing than for any other corporate function. Harrah’s and Cap One are Unica customers.
  • There is some overlap. After all, a dashboard is really just a type of report. And ad hoc analysis often starts in a report Web analytics Other: web analytics, segmentation, lead scoring, biz rules search and analyze large volumes of data with the objective of establishing relationships and identifying patterns
  • Channel evolution: towards more measurable, direct ones. Online also produces more survey data, up to 50% by some accounts. But not necessarily higher quality. Online importance data point: B-to-B customers are turning to the Web in growing numbers to find distributors for the materials they need, according to a survey of engineers, technical buyers and scientific professionals conducted by vertical search engine GlobalSpec. Seventy-three percent of respondents said they go online first to find new distributors, using general and vertical search engines and online directories of industrial manufacturers. Maturity of b2b: adopting marketing tools proven to work elsewhere (bleed over) Marketing’s role in driving revenue seems to be growing
  • How Much to Budget for Database Marketing vs. Lead Generation, Sep 10, 2006 10:46 AM , By M. H. "Mac" McIntosh  from Multichannel Merchant Most b-to-b marketers appear to spend 90%-95% of their budget on image, awareness, and sales lead generation programs, and only 5%-10% on database marketing. Data quality: Forrester: 28% identified data quality and availability as a “Top 5” challenge* Data complexity More address elements with which to deal Poor “grouping” (employee -> division -> company) the churn of business contacts at titled positions and within companies Incomplete fields B2B process Acquisition-generated system duplication Lack of access to critical data (e.g. transactions) Lots of people enter data (sales, marketing, tech support) = tough to have quality standards General lack of concern/focus: data is sales-oriented. B2B DBs more often used for inquiry mgmt than response tracking and analysis: (45%) of respondents said they use their databases for campaign response tracking and analysis. 61% of respondents said they use databases for inquiry management, reflecting the criticality of pipeline management in B-to-B. only 31% of B-to-B marketers seem to be focused on data hygiene. Source: Some 192 B-to-B marketers were polled in June by e-mail for this survey by Ruth P. Stevens and Bernice Grossman Buying process: multi-touch, sales-driven, etc. (more on the next slide) Resources: mainly people, also tools User adoption: applications designed for specialists
  • B2B buyers include paid professionals. Marketers find them much harder to reach. Less risk in B2C purchases Net: More complex data and data relationships to manage. Parent, subsidiary, division, department sales/marketing entanglement time duration, multiple people = hard to determine what drives purchase
  • Bigger are more worried about measurement and lead quality. Also: leveraging online channels; customer experience. Understand customers/prospects, make decisions Drive demand (target, execute)
  • Web analytics?
  • According to a new study by The CMO Council . While 56 percent of vendors consider themselves very customer-centric, only 12 percent of customers agree with that assessment.
  • The bottom tier are casual consumers of reports, supported by the middle layer of the campaign execution Analytics team handled the production reporting in Cognos or BO, the statistians handling the modeling, the researchers and often a small team that is called or focused on customer insight – 3 or 4 people who doing nonthing but provide ad hoc customer analysis
  • The analytics team is using a set of specialized analytic tools – that vary in capabilities but are horizontal in the kinds of analysis such as customer, financial, marketing modeling, fraud modeling. The process tends to start with an email request. The analyst then uses either sas, sql coding or cubes to pull the data and create a spreadsheet that is emailed back to the inquirer, The bottom team will submit requests and often it may be a week or two before they get their results. It will often come back as either a report or spreadsheet with the results.
  • Issues Many questions go unanswered. Self filtering – fastest ones can take a day or two but usually a week or more. So users don’t always ask because not worth it. Analysts don’t always have time 8 out 10, I am looking for a result. But 2 times out of 10, I need to do some analysis – and the spreadsheet format doesn’t make this easy for them From an analysts perspective, this isn’t ideal. Many of the queries are rather pedestrian and not where they want to spend their time Thus it’s not ideal for anyone
  • Why is it like this? Starting from the right, there is usually dispersed data and users need SAS or sQl to pull the information together Secondly, the data is too complex. Nordstrom is a good example. They create cubes and use BO initially imaging users could do this themselves. But users can’t answer a simple like how many blue sweaters did I sell in the Seattle store. For example, what is blue? Is aqua blue? Users need to know how to accumulate and use the data Tools them selves are not amenable to occasional use. Even Cognos cubes are difficult because the interface is primarily rows and columns. A good example is looking at things by geography. For example, in the US, the data is represented as a list of rows and columns, by state, usually alphabetic, not even in any logical order. Solutions Invest in the data Marketing-specific tools Data flexibility Visual Connected to execution Invest in analytical skills
  • Typically costs $25,000 plus for a model. Need to be redone annually at least. Who influences which types of purchases? Response modeling Assuming enough sales history, promotion history Easier for more transactional businesses
  • “ The pot of gold at the end of the demand creation rainbow is a system that will allow marketers to collect data regarding scoring attributes and behaviors and identify who has a higher propensity to qualify to opportunity.” Not here today but “on its way.” SiriusDecisions Field Marketing 2.0 Get definition from YL’s book? 25% b2b in the survey? Gartner: 1-5% current penetration – “adolescent” 2-5 years to mainstream “ High” benefit eventually
  • Why harder B-to-B files often are small, which can limit opportunities to do split testing and data modeling. * B-to-B data degrades more quickly than consumer, causing greater uncertainly in analytical operations. * Business data is complex and harder to work with. * B-to-B files frequently contain incomplete records. For example, a billing address might be shown as “See Judy in Accounting.” the rather low rate of model use points up the difficulty of campaign replication in most business marketing situations. Since campaign conditions change so rapidly, modeling is used less to predict campaign results and more to understand the nature of a customer file.
  • Wearguard-Crest. Use Campaign, Model, NetInsight Telesales people spent too much time figuring out whom to call and only 40% of the time calling. And, they still targeted the wrong companies often or at the wrong time. Also targeted “mom and pop” segment where customer value varied most dramatically. Some customers weren’t profitable. Others clearly bought a specific times of the year. Leveraging this insight with Affinium improved marketing results. Response modeling, customer value The scoring process incorporates various sales indicators and 33 other customer attributes, some augmented with third party data from Dun and Bradstreet
  • Using Affinium Campaign, Marketing then implements business rules that suppress customers based on attributes such as new orders, customer preferences, and recent calls. They then segment customers, based on length of time as a customer, and incorporate the suppression rules, segments, and run-time predictive models of customers most likely to purchase, in order to discover the best prospect and offer candidates – all in a “lights-out” fashion. Affinium drives suggested calls into the order-entry system that the reps use based on predictive modeling scores Also Improved customer service through better-timed outreach
  • A.k.a “dialog marketing” Use airline email example? Event-driven: not just automated “thank you for registering”, which 60% say they do according to Forrester 2005 <20% say programmed/automated lead nurturing according to Forrester 2005
  • Marketing new products to independent and captive insurance agencies Hold seminars about these products at different locations across the country. Hundreds of these each year. Define seminars as offers, with times and locations and agendas as variable offer attributes Email invitation templates are personalized with name, etc. Upon click-through, Interact calculates the “best” offers (specific time/locations of seminar) based on that individuals address and the date and builds the landing page in real-time from a standard template (so marketers don’t have to build templates for every one) Want the closest locations More than 2 days out (they need a few days for the logistics of adding a new attendee) Automated email follow-up (thank you for registering, reminder, thank you for attending/sorry you couldn’t attend) Email invite can be forwarded (viral marketing), in which case the registrant/attendee may represent a new prospect for the company. Follow-up emails come from the regional sales manager. Prospect goes into Leads for follow-up.
  • B2B interest: Forrester Q4 ‘06 survey: >60% said real-time or event-based marketing is a major tech theme for B2B marketers. 11% said top theme, #4 overall. Site optimization, inbound interaction management, web interaction management represent 3 of the top 10 planned technologies. Too ambitious Dick York/Darrin
  • B2B DBs more often used for inquiry mgmt than response tracking and analysis: (45%) of respondents said they use their databases for campaign response tracking and analysis. 61% of respondents said they use databases for inquiry management, reflecting the criticality of pipeline management in B-to-B. only 31% of B-to-B marketers seem to be focused on data hygiene. Source: Some 192 B-to-B marketers were polled in June by e-mail for this survey by Ruth P. Stevens and Bernice Grossman Our survey: Only 29% can easily access and use program performance data to guide future Only 33% can confidently account for the value driven by marketing And that’s for MEASURABLE programs!
  • 2005 survey of 124 marketers (b2b and b2c) B2B DBs more often used for inquiry mgmt than response tracking and analysis: (45%) of respondents said they use their databases for campaign response tracking and analysis. 61% of respondents said they use databases for inquiry management, reflecting the criticality of pipeline management in B-to-B. only 31% of B-to-B marketers seem to be focused on data hygiene. Source: Some 192 B-to-B marketers were polled in June by e-mail for this survey by Ruth P. Stevens and Bernice Grossman
  • Don’t care about operational reporting. A word on metrics: Need to map buying process to find the right places to measure Need a single set of agreed-upon metrics for sales & marketing Measure engagement (Forrester defines) Metrics need to matter to the board room, not internally-facing mktg metrics
  • A word on metrics: Need to map buying process to find the right places to measure Need a single set of agreed-upon metrics for sales & marketing Measure engagement (Forrester defines) Metrics need to matter to the board room, not internally-facing mktg metrics
  • B2B DBs more often used for inquiry mgmt than response tracking and analysis: (45%) of respondents said they use their databases for campaign response tracking and analysis. 61% of respondents said they use databases for inquiry management, reflecting the criticality of pipeline management in B-to-B. only 31% of B-to-B marketers seem to be focused on data hygiene. Source: Some 192 B-to-B marketers were polled in June by e-mail for this survey by Ruth P. Stevens and Bernice Grossman The 2007 Lead Generation Survey , conducted by Bulldog Solutions. The survey of almost 700 marketing and sales executives found that 81% of respondents currently measure the results of their lead generation activities, but 54% of those who say they measure results rated their ability to apply the information from previous activity as fair or poor.
  • Retail: match online product research with offline purchase. Banks: understand email effectiveness without having to use links (which are dangerous because of phishing). If someone logs in to your site 5 seconds after opening your email, you can make a firm attribution. Catalog: attribute sales made online to catalog mailings when source code isn’t used. Response attribution details
  • Act on insight: Do more of what works, less of what doesn’t Drill in to WHY things work or don’t to learn deeper lessons The 2007 Lead Generation Survey , conducted by Bulldog Solutions. The survey of almost 700 marketing and sales executives found that 81% of respondents currently measure the results of their lead generation activities, but 54% of those who say they measure results rated their ability to apply the information from previous activity as fair or poor.
  • Citrix iForum Customer Conference Yearly event in October Campaign Tracking Objectives Activity of Customer Conference site Campaign effectiveness Paid registrations SEM achieved the most registrations Had the lowest cost-per-registration Four keywords drove 80% of registrations All keywords driving registrations were generic (remote access) No branded keywords (Citrix) drove registration Online Advertising Had the highest cost-per-registration One campaign had only one registration at a total cost of $20,000 Two campaigns had no registrations
  • Pontiac: TV ads directing people to specific search engines searches (product or brand) are very big.  For example, Pontiac runs a TV add that specifically closes with a call to action to  “Google Pontiac".  The conversions are 4-6X higher than normal search engine.
  • The New Imperative The Role of Data and Analytics in B2B ...

    1. 1. The New Imperative <ul><li>The Role of Data and Analytics in B2B Marketing </li></ul><ul><li>Andrew Hally </li></ul><ul><li>Vice President, Segment Marketing </li></ul><ul><li>Unica Corporation </li></ul>
    2. 2. The Strategic Nature of Analytics “ Organizations such as Amazon, Harrah’s, Capital One and the Boston Red Sox have dominated their fields by deploying industrial-strength analytics across a wide variety of activities.” “ Organizations are competing on analytics not just because they can – business today is awash in data and data crunchers – but also because they should. At a time when firms in many other industries offer similar products and use comparable technologies, business processes are among the last remaining points of differentiation. And analytics competitors wring every last drop of value from those processes.” Thomas H. Davenport, “Competing on Analytics,” Harvard Business Review, 1.06 At the other end of the spectrum…
    3. 3. Types of Analytical Tools Ad hoc Analysis Data Mining Statistically analyze data to find patterns and relationships Marketing Execution Shift budgets, re-direct resources, personalize interactions, etc. Interactively querying data to understand “why” Dashboards Visual display of KPIs that monitor business health Drill in to reports for more detail Understand “why” Spot issues & opportunities Take action Predict behavior Structured Reporting Regular distribution of information with infrequent change to definitions or format
    4. 4. Trends Driving B2B Analytics <ul><li>General </li></ul><ul><ul><li>Channel evolution </li></ul></ul><ul><ul><li>Marketing accountability and spend shift </li></ul></ul><ul><ul><li>Maturation of marketing software </li></ul></ul><ul><li>B2B-specific </li></ul><ul><ul><li>Maturation of B2B marketing </li></ul></ul><ul><ul><li>SaaS/on-demand business model </li></ul></ul>
    5. 5. Barriers to B2B Analytics <ul><li>Data, data, data </li></ul><ul><li>Buying process differences </li></ul><ul><li>Resources </li></ul><ul><li>Poor user adoption </li></ul>
    6. 6. B2B Isn’t One-Size-Fits-All x,000,000 x,000 Customers Individual Many Decision makers Retail, dealers Direct, VARs Channels Low High Cost Contractual Retail Pricing One Many Entities Instantaneous Considered Decision None Multilevel Purchase approval
    7. 7. B2B Marketer Top Challenges Source: Forrester’s Q2 2006 B2B Marketing Effectiveness Online Survey “ What are your top five B2B marketing challenges?” Generating more leads Improving lead quality Measuring marketing results Reaching decision-makers 54% 53% 48% 44% Understanding prospect behavior 27% Deepening customer relationships 40% … … Measuring Results Driving Demand Customer/prospect Insight
    8. 8. Analytics for Top Challenges Results analysis <ul><li>Response Attribution </li></ul><ul><li>Results reporting </li></ul>Measure Results <ul><li>Personalization </li></ul><ul><li>Interaction management </li></ul><ul><li>Event-driven marketing </li></ul>Operational reporting Drive Demand <ul><li>Segmentation </li></ul><ul><li>Predictive </li></ul><ul><li>Decision-making </li></ul><ul><li>Segmentation </li></ul>Customer metrics Customer/Prospect Insight Operational Data mining Ad hoc Analysis Reporting
    9. 9. Ad-hoc Analysis <ul><li>Understand customers/prospects to: </li></ul><ul><ul><li>Make decisions </li></ul></ul><ul><ul><li>Segment customers </li></ul></ul><ul><ul><li>Identify opportunities </li></ul></ul><ul><ul><li>Target programs </li></ul></ul><ul><ul><li>Guide product/service development </li></ul></ul><ul><ul><li>Improve the customer experience </li></ul></ul><ul><li>Why aren’t we leveraging the opportunity? </li></ul>
    10. 10. Typical analytics organization Analytics Team Campaign Execution Team Marketing Managers Product Line Mgmt Segment Managers Channel Managers Senior Mgmt Consumers Customer Data Central Marketing Functions <ul><li>Customer insight </li></ul><ul><li>Modeling </li></ul><ul><li>Reporting </li></ul><ul><li>Marketing research </li></ul><ul><li>Consume production reports </li></ul><ul><li>Make requests for analysis, campaigns </li></ul><ul><li>Some are occasional direct users </li></ul>Web Data Other Other Data
    11. 11. Typical ad hoc analytics process Product Line Mgmt Segment Managers Channel Managers Senior Mgmt Consumers Central Marketing Functions Analytics Team Marketing Managers Customer Data <ul><li>Consume production reports </li></ul><ul><li>Make requests for analysis </li></ul><ul><li>Some are occasional direct users </li></ul>Web Data SQL <ul><li>Update reports </li></ul><ul><li>Email request for analysis </li></ul><ul><li>Or, reply with results </li></ul>Other Data
    12. 12. Ad hoc analytics pain points Product Line Mgmt Segment Managers Channel Managers Senior Mgmt Consumers Central Marketing Functions Analytics Team Marketing Managers Customer Data Web Data <ul><li>Many questions go unanswered </li></ul><ul><li>Waiting on analysis hinders responsiveness </li></ul><ul><li>Limited ability to drill, ask follow-ups </li></ul><ul><li>Hard to act directly on insights </li></ul>Time spent on simple queries, not strategic analysis SQL Other Data
    13. 13. What are the root causes? Product Line Mgmt Segment Managers Channel Managers Senior Mgmt Consumers Central Marketing Functions Analytics Team Marketing Managers Customer Data Web Data <ul><li>Many questions go unanswered </li></ul><ul><li>Waiting on analysis hinders responsiveness </li></ul><ul><li>Limited ability to drill, ask follow-ups </li></ul><ul><li>Hard to act directly on insights </li></ul>Time spent on simple queries, not strategic analysis <ul><li>Data: </li></ul><ul><li>Dispersed </li></ul><ul><li>Too complex for business user use </li></ul><ul><li>Tools : </li></ul><ul><li>Too generic, hard to use for business user use </li></ul><ul><li>Require extensive IT sup’t </li></ul><ul><li>Fear and desire </li></ul><ul><li>Skills </li></ul><ul><li>Fear </li></ul>Other Data SQL
    14. 14. Data Mining in B2B Response Modeling Value Prediction Segmentation
    15. 15. B2B Adoption of Data Mining <ul><li>Use of data mining/predictive analytics in: </li></ul>Overall The tools and proof are there, so why slow adoption? Source: Forrester Q4 ’06 survey B2B Only
    16. 16. Challenges to B2B Data Mining <ul><li>Data complexity, quality and degradation </li></ul><ul><li>Complex sales cycle harder to model </li></ul><ul><li>Smaller databases </li></ul><ul><li>Less campaign replication </li></ul>
    17. 17. Mini Case Study <ul><li>Data mining objectives </li></ul><ul><li>Focus mid-market telesales on the best opportunities </li></ul><ul><li>Better target “mom and pop” catalog mailings </li></ul><ul><li>Modeling overview </li></ul><ul><li>Response modeling for purchase likelihood </li></ul><ul><li>Leveraged a new data warehouse </li></ul><ul><li>Catalog maillings: seasonal models based on past purchases, region, etc. </li></ul><ul><li>Telesales: key sales indicators plus 33 other customer attributes </li></ul>
    18. 18. Mini Case Study <ul><li>Execution </li></ul><ul><li>Also target based on preferences, recency, profitability </li></ul><ul><li>Process runs “lights out” on a daily basis </li></ul>Sales efficiency 10 - 20% Reduced mail cost $1 - 2m Results “ Our business has become much more efficient and customer-centric. Costs are down and our customers are experiencing better service. Greg Nathan, Sr. Manager, Database Marketing, Business Sales Group
    19. 19. Operational Analytics <ul><li>“Automated analytics embedded into business processes” </li></ul><ul><ul><li>Email personalization </li></ul></ul><ul><ul><li>Event-driven targeting </li></ul></ul><ul><ul><li>Automated “lead nurturing” campaigns </li></ul></ul><ul><ul><li>Real-time interaction management </li></ul></ul>
    20. 20. Mini Case Study: Driving Event Attendance Customer/Prospect Marketing Team Personalized Registration Thank You for Attending Attend Seminar Email Invitation Sales Outreach
    21. 21. Premature B2B Interest? Automated Lead Nurturing Email Personalization Real-time Interaction Management Web Optimization B2B Readiness Event-driven Marketing
    22. 22. Why is it so Hard to Measure Results?!? Forrester Research survey “ How do you measure the results of lead development efforts?” No reliable means for measuring Track responses, but can’t connect to sales Actively working to “close the loop” Closed-loop process 37% 36% 16% 11%
    23. 23. Measurement Barriers Source: Unica/AMR Research survey
    24. 24. Key Components of Measurement <ul><li>Metrics </li></ul><ul><li>Based on buying process </li></ul><ul><li>Sales-marketing alignment </li></ul><ul><li>Customer, engagement, business-oriented </li></ul>Response Attribution Flexible, cross-channel rules automatically link behavior with marketing drivers Results Reporting Results and ROI by audience, program, channel, offer, etc. Data Contact history, response history, transactional history
    25. 25. What are the Right Metrics? <ul><li>Mapped to the buying process </li></ul><ul><li>Sales and marketing agreement </li></ul><ul><li>Business-oriented </li></ul><ul><li>Customer-oriented (long-term) </li></ul><ul><li>Engagement-oriented </li></ul>
    26. 26. Response Tracking and Attribution <ul><li>Technology can help: </li></ul><ul><ul><li>“ System of record” tracks all touches </li></ul></ul><ul><ul><li>Flexible access to disparate results data </li></ul></ul><ul><ul><li>Articulate “rules” for attributing revenue to touches </li></ul></ul><ul><ul><li>Automate report generation and sharing effort </li></ul></ul><ul><ul><li>Integration with analysis and targeting tools facilitates application of learnings </li></ul></ul>Easier Harder <ul><li>Individual decision </li></ul><ul><li>Quick sales cycle </li></ul><ul><li>Few “touches” </li></ul><ul><li>Long sales cycle </li></ul><ul><li>Multiple decision-makers </li></ul><ul><li>Numerous “touches </li></ul>
    27. 27. Automating Marketing Measurement Marketing “System of Record” <ul><li>Costs </li></ul>Planning & Design <ul><li>Offers </li></ul><ul><li>Campaigns </li></ul>Cross-channel Execution <ul><li>Contacts </li></ul><ul><li>Delivery </li></ul>Contacts Match Results Reporting <ul><li>Purchases </li></ul><ul><li>Transactions </li></ul>Transactions Closed-loop, test & learn capability
    28. 28. Analysis Results to Apply Learnings
    29. 29. Mini Case Study: Citrix Online Registration Pay-per-click Online Ads Site Banner Email Direct Mail Promotion Citrix iForum Conference Email Early Bird Ends .com Banner SEM, Online Ads Direct Mail
    30. 30. Evaluating Offline Ad Campaign Effectiveness <ul><li>Lead demographics by industry </li></ul><ul><li>Indirect “lift” by day </li></ul>Lead form
    31. 31. Thank you! <ul><li>Andrew Hally </li></ul><ul><li>Vice President, Segment Marketing </li></ul><ul><li>Unica Corporation </li></ul><ul><li>[email_address] </li></ul>

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