Many factors contribute to governance and business boundaries can quickly be broken down if you approach governance in business silos.  As you walk through your process of data collection you’ll quickly find that what is considered the preferred source of data may not be generated by the team that determines what should stay, what should be modified, and what should go.  In fact, depending on how you view the data, conflicts arise as to what is considered accurate, appropriate, of the contributing factor in decision and business point of view.

This is something I’ve run into recently when building a business intelligence solution for web analytics.  Even within my own department of advertising executives, views of what transactional data should be considered the record of source is up for grabs depending on who is the recipient of the information and how it is used.  Levels of accuracy vary depending on when data is needed, how it may be used for marketing optimizations, or if it will be used to actualize spending for billing.  Throw into the mix that data feeds coming from vendors are constantly changing as they actualize transactions over the course of days, weeks, and even months, and finding the truth in the data becomes a challenge that defies religious opinion on the subject.

Sorting through the challenges of governance to determine what makes data reliable requires looking at a variety of factors and allowing for multiple views and uses.

• Reliability of source
• Time of collection
• Actualization
• Business process affected/use of data in decisions
• Degree of accuracy required

If you will notice, I do not include ownership.  This is the artificial governance.  Ownership in establishing governance only serves to create a framework around the above factors that creates credibility.  Ownership, and then the transformation to stewardship, serves to continuously monitor, enforce, and improve governance around data needs.

Start your data management off on the right foot, don’t confuse ownership with governance.

Internet marketers need to think like their program marketers.  They need to be asking the same questions and align to their organizational goals.  The way to do this is through analysis of converter profiles and behavior analysis.  This way you do what we all say we should do:  target the right people, at the right time, with the right message/offer.  It isn’t enough to drive volume, you want to drive quality in that volume.  It is much more expensive to acquire new unfamiliar customers than it is to convert those that are already considering you or are you customers.  This is true whether it is internet marketing or offline marketing.  Why convert customers that will make a small purchase, then dump you later when you can convert a customer that is interested in your products now and later on?  Keep the short-timers in the mix at an optimal rate to maintain revenue, but it’s the long term customer that will be profitable in over time.

Think about your over arching marketing plan.  A key component is who your ideal customer is and how do you bring them in, keep them, and grow their value.  Somehow this is getting lost when you put in your big ad spends on paid search and display advertising.  In the tactic, the measure is at the ad group, placement, key word, creative, offer/call-to-action.  It is all about optimizing increases in conversion volume to site at the lowest cost.  We need to flip this over and go back to also looking at if we are converting the right people.

Converter Analysis

Take your campaign analysis down one more level.  Look at who is making a purchase on your site and what they are purchasing.  Are the purchaser profiles aligned to your broader targeting strategy?  If you were going after 30-something new mothers in affluent metros, were a high proportion of those purchases in this group, or were they 50-something grandparents in retirement communities?  The first may offer repeat purchases as new mothers will continue to purchase items over the growing years of their babies.  Grandparents or relatives may only complete a one-time purchase for the baby-shower.  You’ve already done the research to say where to get the most opportunity short term and long term, make sure your internet marketing effort supporting this.

Behavioral Analysis

Now that you know what your converted customer looks like, move upstream to their internet behavior.  Analyze your ad groups and key word searches and compare customer segments.  Recognizing attribution as it pertains to segment targeting will allow you to optimize more surgically.  On the surface particular tactics may appear to be driving the greatest conversion.  But, this may not be the case as it pertains to who you want to attract.  Through behavioral analysis you’ll better position your ad spend for behavioral targeting and optimize your online display dollars.

Becoming a better internet marketer is as much about effectiveness as it is about efficiency.  Effectiveness comes not only from volume of conversions but having the right volume of quality conversions.  Optimize not only to the volume.  Optimize to the segment you wanted to reach in the first place.

It is not as simple as recreating your Excel spreadsheets in a database and copying the graphs to Powerpoint.  Forget what your analysts tell you.  The data isn’t the most important thing in this process.  Your business rules governing your data is the most important.  The business rules stem from your process, your plan, and the point of view of your business.  Make sure your establish your marketing plans and business rules as the cornerstone to your reporting needs BEFORE you go right to the data.

Most business intelligence projects are emerging from mature processes and the topmost functional areas that have application systems capturing and storing data as it happens.  There is a consistency around data elements.  For instance, finance, sales/marketing, customer service all work off customer data with sales transactions and relationship inter-actions.  The reason is that the application manages the data.  Where this breaks down is in the new world of cloud computing and SaaS solutions managing micro components of your business.

Your headaches in understanding marketing performance stems from this increasingly disparate view of your activities.  Traditional marketing is housed in your CRM system (ex. Siebel, SAP).  Website marketing is house in your web analytic suite (ex. Omniture, Webtrends).  E-Commerce might be managed with your E-Commerce provider.  Interactive marketing may be managed by your ad service (ex. MediaPlex, DART) or Google.  Each of these services have their own discrete view of the world and provide you with canned reports.  First and foremost all these various systems need to be bring data into a central repository before any consolidated reporting can happen.  This is where most reporting projects begin and move right to the reporting design and implementation.

Be careful.  This is the pitfall.

What makes the reporting in these disparate systems work is the business rules and processes that generate the data.  How a campaign is set up and tracked in one system can be very different than the other.  Cost management will vary by activity as well as marketing cost may be flat fees or operate through exchange systems with different billing methods (ex: by time, conversion, packaging). You want your campaign plans enforcing the management and reporting of activity across all marketing channels.  If you have not created this framework, you risk accuracy and completeness as feeds can have issues in delivery or have conflicting data that needs to be reconciled and/or adjusted prior to reporting.

As you begin your marketing performance reporting project, bring your sample reports and spreadsheets to the table during business analysis.  But, you also want to be sure you bring your marketing plans and business rules to the table as well.  This will ensure accruacy of reporting and mitigate issues in design, development, and implementation of your new dashboards.

The first problem is that measuring engagement often has more to do with the amount of time spent on site and the amount of view, clicks, and level of content reached.  On the surface, this is a great first step.  When looking closely, flaws abound.  The reason, what is the online experience trying to achieve?  If the purpose is a landing area that drives purchase conversions, then more time on the site and an increase in pathing may actually be an indication of less qualified visitation. If the purpose is education and a first step into creating a customer relationship, then more time on the site, activity, and depth of knowledge seeking can be a good thing,  However, to be realistic, have you looked at your SEO and SEM statistics lately?  My guess is that around 70% of those visiting your site are direct visitors or searching on branded keywords.  That being the case, visitors already know a good deal about you prior to coming to your site and the more time and research they do might also not be a good thing if they are comparison shopping.

Sounds a bit dire, right?

To counter this, web analysts are starting to take a look at measuring actions as they relate to conversion.  Simply spending time on the site, views, or measuring clicks isn’t considered viable and predictive.  However, if desired actions are achieved such as downloading high value content, sharing content, participating in discussions, or taking actions that are highly linked and indicative of purchase behavior, then tracking at this level is more valuable.  Actions can be more connected and aligned to desired results and predict conversion.  Right?  Maybe.  The issue arises of clearly understanding actions that predict conversion to sales or customer value.  The other issue is that measuring the number of actions also isn’t that far off from measuring page views, clicks, and time spent.  It might be more meaningful in that it is a validated initiative, but again, is more actions a good thing?  Once again, as with traditional metrics, at the end of the day, what is your site or landing area intended to do?

Measuring engagement should actually take into account both methods for a hybrid approach.  How this hybrid is determined once again depends on what your desired outcome for the website or online experience should be.  At the simplest level, starting with actions taken and tracked as the foundation of a predictive model is a more sound approach.  These are steps in a desired process for conversion, regardless of what your conversion intent is, that are reliable and accurately measured.  However, actions are part of a process and thus need to be ordered and weighted accordingly.  Processes are relatively linear in fashion and assigning a weight based on the step in the path is important.  It can be a simple distribution or multiplicative, but a step does have relevance and weight.  In a hybrid approach, we also want to introduce the traditional aspects of views, time spent, and clicks.  Starting with views and time spent, leveraging these as coefficients in the model will provide a better perspective on weight on desired actions and ultimately the desired outcome.  Essentially, views act as impressions that influence behavior and time spent introduces the amount of exposure necessary to trigger a desired result.  Taking from online display advertising effectiveness, banner ads as an influencing factor for awareness and conversion increases with exposure even if no action is taken to click through.

That leaves clicks.  This traditional metric introduces a duplicity element that needs reconciliation.  It is important to carefully introduce this measure into the model as it can inflate engagement metrics and thus over forecast results.  Clicks also can be an issue as it is typically a component of measuring effectiveness of ad spend.  What needs to be determined is if the click is associated to intended actions taken on site and avoid double counting or inaccurately measuring ROAS.

Ultimately, engagement is an indicator of a desired outcome and not the outcome.  Combining traditional site tracking methods to weight and adjust models predicated on process actions will create a more accurate predictor of outcomes.

Demand and lead generation steps have typically progressed from response to lead pass without adequate filtering or analysis that a lead is ready to engage in the sales process.  This has hurt marketing’s credibility in generating real value to the pipeline.  It has put the work on sales to ‘clean’ the database and have them focus energy on leads that aren’t interested or ready for personal connection and may be of lower value than cold calling.  Additionally, some companies try to alleviate this by adding a telemarketing stage prior to a lead pass to personally assess and qualify a lead for the pass.  This can be a costly investment for marketing if again, it is putting leads into this step of the process before leads are fully baked.  Yet, that doesn’t have to be the case.  Properly analyzing and defining leads or groups of leads by their activity within an account can offer sales insight that puts them closer to the opportunity.  This is where lead nurturing can be a strategic effort rather than a tactical process.

Traditional lead tracking reports show a linear funnel from response to disposition within a campaign or program which mimics the linear aspect of the lead process.  In reality, leads have most likely been associated across campaigns, social media marketing interactions, organic web visitations, and even events or interactions with sales and other organizations.  How leads interact, where they go, the frequency, and topic concentration tells you a lot about how ready they are to enter a sales engagement process.  Additionally, compared and correlated to other leads within the same organization, you get a good picture of account readiness and opportunity.

This analysis in many cases is conducted to create target segments as launch pads for new campaigns.  Leveraged within a lead nurturing process, it can be the used as the decision point for when it is best to pass a lead to sales.  It becomes what qualifies the lead to move on vs. relying solely on a single response point on its own or in a linear context.  In fact, analyzed properly, reports and dashboards can be provided to sales that provide a picture of high opportunity areas within their accounts that they may not have seen.  For instance, an up-tic in white paper readership and participating or scanning of social media marketing content on products within an account might provide account managers early warnings that companies are assessing new solutions.  By having a report that provides context on the customer relationship provides sales a greater ability to pick up on the lead nurturing process without having to wait for marketing to pass the lead themselves.

Today, leads are classified as meeting minimum requirements of responding to a campaign and having check boxes of information filled out.  Lead nurturing is really about understanding interactions with your customers and how those interactions are indicators for next steps in the relationship.  Analyzing and recognizing patterns within your contact and account databases is more than identifying segments for targeting new messages and offers.  Used strategically it can be a transition point in your lead pass process improving your ability to generate business and reduce resources and budget through better focus.

Invariably, your database is more than just purchasing/decision maker contacts.  All departments have gathered people’s information depending on the purpose.  It offers a window into your business dealings.  It also offers a window on your ability to market and sell.  Just as you consider vehicles, content, and message to deliver to your database, you also think about who you are reaching and who can be converted.

SOA and MDM initiatives are great because they bring together a full picture of interactions with the customer as well as who is part of those interactions.  But, not all contacts are created equal.  Just as not all customers or companies are created equal.  It is the first thing that is considered when determining targeting strategies.  The size of a database is typically determined based on the silo it is intended to help.  Marketing wants decision makers, finance wants accounts payable, customer support wants end users, investor relations wants analysts and media.  By themselves, these data silos serve a purpose.  Together, they can show a picture of where your awareness, message and brand really are.

A good  test once consolidation of data bases is done, or even within your CRM system alone if it receives lists and feeds from other internal sources, is to classify contacts based on their primary interaction with your company.  Everyone in your database has had a reason to connect.  Bringing these reasons into a standardized category will help determine the value they bring to a marketing program, customer relationship, or evangelist role.  Monitoring the ratios of these groups within a cusotmer relationship and firmographic data can give insight into the ability to grow a relationship, if it is at risk, or there is no relationship and the company serves another purpose.

While as marketers we typically look at the entire size of our database to determine if we have enough contacts to convert to leads, if those leads are weighted towards a low number of companies, or they are not the right contacts, then our efforts can be wasted.  With the cost to acquire customers and contacts expensive, having a mechanism to determine when to purchase lists and how much to purchase will refine the amount of resources and budget needed.  In addition, messaging and engagement strategies can be modified to align to the type of relationship outcome you intend.

So, rather than thinking about personas when you need to target, think about them strategically and as an indicator of the strength of relationship with your customer.

Marketing Effectiveness

In it’s ability to facilitate sales and deepen the customer relationship, time and again, marketers and sales are unable to translate awareness and conversation trends in social media to sales. In addition, I wonder if connection trends, comment ratios, and sharing ratios are really anything but another way to track existing customer relationships. I’ve narrowed down marketing effectiveness metrics to four (4) key themes. In each case, I’m looking for improvements due to social media.

• Improve win/loss ratio – Sales may ultimately be responsible for this metric, but marketing is responsible for lead nurturing which contributes to it. The reality is that the awareness marketing that is happening in social media may not be doing anything but providing another outlet for the same content. Tactics such as white paper promotion and communication of offers may appear to increase leads, but views and registrations may ultimately be with the same people already existing within the customer database. In the end, is the social media marketing tactic really changing customer perception during the sales process to make them choose you’re solution more often? I’m not sure it does.
• Shorten sales cycle – I pose that the sales cycle may actually be lengthening in social media marketing rather than shrinking. Social media appears to be focused more on awareness building than lead generation. This effort is at the beginning stages of the marketing funnel. In fact, because of the conversational nature of social media, it takes longer to convert a ‘getting to know you’ dialogue to a ‘let’s do business’ dialogue. So, instead of coordinating marketing efforts with sales engagement and the decision process, social media is acting more as a fishing net.
• Increase sales – Due to an increased sales cycle, you may be losing time to help close a deal. Solely focusing on lead nurturing vs. lead conversion can have the affect of creating a state of purgatory for potential customers. Social media, in theory, should help expand your footprint within your customer base by improving customer relationships. However, all social media marketing is doing today is proving a facelift to existing customer forums, white-paper libraries, and transitioning web content to blog content.
• Reduce churn – There is much buzz around Twitter’s ability to manage customer expectations and improve customer support. Thus, this translates to reducing customer defection. The issue here is that this isn’t happening in the marketing organization. This is a function of customer service. Where marketing fails is that customers are focused on their business, not yours. Conversations in social media marketing today are still more focused on ‘look at me Mr. Customer’. All the customer wants is for you to look at them. It is an effort for customers to utilize and participate in social networks and gather information in social media. There are still too many places the customer has to go to interact. We make it difficult to solidify relationships by managing multiple properties and outlets to connect.

Marketing Efficiency

There is a real hidden cost to utilizing social media for B2B marketing. It is the cost to do business. Due to the number of ways you can connect to customers, it requires a significant amount of effort to cover and manage all the properties. While you can write a single blog and push it out across multiple communities, the lack of diversity in conversations may hurt more than help. Each community probably has a different DNA. One message is not going to be relevant for all. Thus, you have to produce more content across more topics to be effective.

Another aspect of inefficiency is the art of the conversation. For social media to work, it requires a de-centralized communication web to interact with customers. Sales already has this in place as it is what they do every day. Marketing is smaller and has less resources. This puts pressure on the organization to have personalized attention to carry on a conversation. Marketing needs the ability to respond to comments, participate in groups in a conversational manner, and organize discussions and groups around a multitude of topics that customers are interested in. If you go to forums today, there are few that have real conversations happening. Mostly you see blogging and promotional content being posted. This is because it takes a huge amount of bandwidth to truly be interactive with your customers.

Lastly, there is inefficiency to how marketing manages relationships across multiple social media platforms. Again, the number of venues creates chaos in the ability to recognize a single customer. Efforts are duplicative and can create problems in a cohesive conversation and message. Marketing technology needs to be streamlined to better manage relationships.

What’s Next?

As social media marketing has been the buzz and huge shifts are being made to transition and leverage its potential, B2B marketing organizations need to be mindful of what their business charter is and how they meet their goals through effectiveness and efficiency. Social media is just part of the mix, and as with any marketing effort, you don’t want to put all your efforts into one tactic. If not properly monitored against key business benchmarks it can quickly de-focus your marketing efforts and lead to poor performance.

I’m managing more properties and content that is outside the realm of my corporate environment but I still have to report back and show how it is doing.  The only way I can do this is by using several analytic tools across multiple properties.  I grab the stats I need and punch that into a spreadsheet.  Then, I go to my web analytics reports, grab those stats, and consolidate them with my social media data on my spreadsheet. After that, I consolidate my lead metrics with my internet metrics for a 360° view of my marketing efforts.

It is all very time consuming and open to data entry error.

Business Intelligence is great to track internal process, but it is doing nothing to help track activities outside the corporate environment.  So, I’m stuck with spreadsheets.  Can you help?

How many times have you heard that?  It’s become the standard mantra.  It is so ubiquitous that I don’t think anyone questions anymore the validity of the statement.  It just is.  However, this is probably the hardest part to facilitate when building out you business intelligence practice.  Facilitating decisions is what makes BI stragetic.

Just what is the business decision? What does a business decision look like?

Elements of a Business Decision:

• Purpose:  drive a business outcome – ex: revenue, shareholder value, profitability, market share
• Position:  leads a company, division, department
• Point in Time:  transition along a process or environment

A typical approach during the business analysis phase for BI is to at business decisions across a business process and where questions are asked to change behavior in that process.  Although, the difficulty with this level of granularity is that it is too deep.  These transition points are tactical.  Intelligence across this process and at these decision points is important, but you don’t get the strategic value of BI at this level.  You need to look at the outcome of the process and provide a platform that supports the decision of what to do next.  This is the unstated question.

Let’s take an example.  Sales management will always want a perspective on the pipeline and forecast.  This shows them how they are meeting their numbers quarter to quarter.  However, outside of conversion and volume, there are business decisions that sales managers need to make.  Should they adjust their territories to capture new opportunity or shore up existing business?  Are there changes needed in commissions to incent sales people along certain products and services to improve profitability or revenue?   BI can lead sales management with insights that will guide them to optimize their processes and management rather than just data.

Purpose:  market share, revenue, profit
Position:  sales
Point in Time:  aligned to quarterly pipeline and forecast

To align BI to the business decision it is important to include executives in the discussion.  Get beyond the reports they want to see and ask the question about how they manage their business.  Walk through scenarios of what they ask as changes in the market or the business arise and how information can help them make a decision.  The better able you are to see how they manage their business, the more valuable the BI practice will be to supporting the business.

You have analytics players, enterprise application vendors, business process consultants, and analysts all trying to sell the ‘hype’ of a better way to analyze your business and makes decisions.    SAS wants to sell their analytic solution that really pioneered data mining in businesses.  Oracle and IBM wants to push dashboard solutions that links to business processes and their enterprise applications.  Gartner that tries to tie together people, process, and technology but is really is focused on what technology to buy.  Then, you have consultants that are trying to help you implement the technology even as they document your processes.  The problem is that it’s all boiling down to the one with the best tool wins.

Enter in the ‘Business’ and now you have a problem.  All they want to know is how they can meet their business objectives.  IT is trying to sell the solution and make them understand the technology, and the business glazes over and can’t figure out what to focus on.  I’ve sat in these discussions where IT tells me, “You tell us what to do, we’ll do it.  Don’t worry about the solution.”  It is open ended.  This leads to IT unable to work towards tangible goals and results.  The business walks away frustrated, projects run from months into years, and original budgets are thrown out the window.  I liken these projects to Boston’s Big Dig.

Neil Raden provided a perfect way to get through the fluff and hype that surrounds analytics and business intelligence. See article From BI to Business Analytics, It’s All Fluff

“I don’t like the term business analytics; it doesn’t tell me anything. Frankly, I think business intelligence as a term is downright laughable, too. What does that mean? Is integrating data intelligence? Is generating reports intelligence? Maybe its informing, but isn’t intelligence something you HAVE not something you do? Does doing what we call BI lead to intelligence, or just some information? A long time ago we called this decision support, and that gets my vote.”

So here’s my take on what steps to take when and how to venture into BI and analytic solutions.

Steps:

1. What decisions need to be made?
2. At what point in our business and business processes are these decisions made?
3. What information is needed at these points?
4. How should our applications and data provide this information – triggers or visualization?

See the steps?  It starts with the business decion and ends in the technology.  So, when you begin to review vendors and solutions, make sure you have steps 1,2,3 in mind before you determine how to solve step 4.