Does Data Quality Matter in Social Media?

Data driven marketing is reliant on high quality data, but with the introduction of social media and its pervasiveness in the marketing tool kit, it is easier to engage with your market without having to have correct emails, addresses, or profiles. It begs the question, does data quality matter anymore for marketing in a Web 2.0 world?

I think the answer is, “Yes, but…”

Direct marketing and bottom of the funnel mindset is what most B2B marketers work in as they have been more closely ties to sales goals.  Where sales won’t accept a lead without knowing who it is and the appropriate contact information at a minimum, it has to be collected at every opportunity.  Without this information, marketing also doesn’t have an adequate single view of the customer to profile and segment reliably.  In this context, data quality is critical as it determines if a lead is passed, how to pass the lead, and align the lead to existing opportunities or account profiles.  Name, company, location, phone, and email are the cornerstone to this.

Social media is not outreach, it is in-reach.  It isn’t lead generation, it is relationship generation.  You don’t collect details on your connections and contacts.  You cultivate engagement and conversation.  Without the need to maintain a list of connections in your CRM and the ability to leverage social media organizers like HootSuite to communicate to your community, contact information is somewhat irrelevant.

So, where is data quality necessary?  Having a single customer view that is inclusive of social media profiles and engagement. At some point, us B2B marketers do need to move relationships out of the 2.0 world and into face to face engagements, particularly for complex sales.  At this transition point, the social media profile becomes an invaluable part of the customer view.  Just as CRM captures order transactions, direct marketing interactions, and sale interactions, it also needs to show social media interactions.  Why? The social media interaction is probably more telling of your relationship with your customers than traditional interactions.

The catch? Linking a limited profile from LinkedIn, Twitter, or Facebook to a standard contact profile in CRM can be problematic.  Your CRM system may not have the ability or capability enabled to link the 2.0 world with your customer data. You may not have a social media platform that is capturing what is needed to integrate your customer data between online and CRM.  Or, it does, but integration needs to be established.  Those are just a few examples.

Ultimately, data quality will matter for social media as B2B marketers mature in their use and linkage of 2.0 activities to best practices for lead creation, nurture, and pipeline generation.  We live for now in customer relationship silos, but the real advantage and benefit of social media to show ROI for marketing will be improved integration and profile management across the entire relationship.  As soon as integration is introduced, just as in the past, data quality plays a critical role.

Filed under: b2b, CMO seat, crm, data quality, marketing technology, social media, crm, data quality, social media

Data Governance More Than Ownership

When kicking off data management initiatives a large and key component is establishing the data stewards that represent the data that is collected, managed, and leveraged in business intelligence.  By having these data stewards, and subsequently a data management committee, companies feel safe that the proper data governance practices are going to be put in place.  Not so.  Ownership (=Stewardship)  does not equate to governance.

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.

Filed under: business intelligence, data quality, data governance, data management, data quality, data stewardship

Ensuring quality data from service providers

For those of us that have lived, eaten, and slept with data quality and data management it is hard to fathom that there are still pockets of those that have yet to define a solid foundation of data quality and data management best practices.  It is even harder still to take a step (leap) back into the roots of how data quality and data management issues all began.  Well, let me tell you, those pockets of organizations are alive and well in the most unlikely places – those companies that are providing data.

To be fair, there are some amazing companies out there that provide information and data that we use to improve and enhance our own data or take to analyze independently.  They may not be perfect (no one is!).  Though, they have defined themselves as servicing organizations with “better quality data” and stand by it with best practices of their own.  But, as enterprise organizations and even mid-sized companies have jumped on the band wagon and adopted sophisticated processes, solutions, and people that are dedicated to better information, there are still a significant number of services providers that lack the skills, tools, and practices that would ensure reliable information to measure our performance, understand our market, and take advantage of new opportunities.

At the end of the day, the data and information we source needs to be reliable.  It is important to guard yourself when both contracting with service providers and when you receive data.  Simply relying on the fact that the data is of high quality when you receive it is not good enough.  You need to be vigilant during the sourcing of providers as well as clearly defining how you can ensure what you received is what you paid for.  Here are some things to consider and ask when working with data providers:

• How do they collect their information?
• How do they verify that the information is valid?  What process, sources, and analysis is used?
• Are they providing data to other customers for the same purpose you need the information for?  How many/what portion?
• What is their repeat business rate?  Who are their top customers?
• What purposes are their customers using their data?
• What do they do to verify and validate your data prior to providing it to you?
• What do they do to verify that the data they are providing is complete?
• What guarantees do they or will they provide that the data meets your specifications and quality standards?
• What is required on your end to validate that the data is accurate and reliable?
• If you are purchasing tracking data (real time/period feeds), what initial and regular testing processes used to verify proper data transfers?
• What is required on your end to ensure the data transfer is working initially and ongoing?

What have you done to ensure data from service providers is what you want?

Filed under: data quality, data management, data quality, data services, data vendors, vender management

B2B CRM: The Right Contact Mix for Your Customer Relationship

You’ve spent years gathering contacts into your databases.  You’ve implemented a data quality practice that is now starting to give you a solid picture of your universe.  It is now time to classify your contacts.

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.

Filed under: business intelligence, crm, data quality, marketing operations, b2b, business-to-business, crm, customer database, customer management, Customer relationship management, list management, marketing, marketing database, Service-oriented architecture, targeting

Is Twitter an Effective Direct Marketing Tool?

There are many uses for Twitter, but a significant use is to share content.  So, if you are a marketer and trying to get reach and conversion by feeding your blogs, white papers, and event invites through Twitter, what is the click-through rate?  How does it compare to email and direct mail?  Just how effective is it?

Pear Analytics just released a post looking at just that.  Their conclusion is that Twitter only provided a  click-through rate equivalent to direct mail.

… [A] “useful” tweet has the following characteristics:

-a shelf life of about 1 hr 15 min, and then it “dies”
-1 to 2% click-through rate on links

Which means that this is not a whole lot different than direct mail for example, without out the cost of course.

Ouch!  Alright, so it didn’t cost anything except manpower, but it is supposed to be better than direct mail and even email due to the ‘viral’ aspect of being within a social network.  That’s the hype.  That’s what the creative gurus are telling us. 

The issue with Twitter as a direct marketing tool has more to do with the fact that you cannot manage your list.  You may be able to manage your own follower and following list, but ultimately you are relying on the good will of others in the network to get out the message.  The way you manage your Twitter list is different than others manage theirs.

A big factor of success in direct marketing is the ability to slice, dice, and segment for a targeted approach.  It is surgical and scientific.  Even when you purchase lists you account for quality and alignment to your purpose, message, and content.  This simply is not manageable in Twitter if your follower’s networks are built for size rather than quality.  You can at least have negotiate money back if lists your purchase from vendors have quality issues.  But, Twitter lists are free.

Social networks like Twitter are great to keep high quality leads and customers close and then leverage to build your databases through early stage outreach.    When new leads do come into your social network, check for quality as this will tell you if your viral channel is high quality as well.  Then, If social network connections meet a threshold for quality, migrate to your central marketing database for lead nurturing.

 

 

Filed under: customer relationship, data quality, marketing operations, marketing technology, metrics, networking, social media, Direct marketing, lead conversion, lead generation, lead quality, marketing database, marketing metrics, social media, social media marketing, social networks, Twitter