I just attended the Destination CRM conference in NYC. It was great - not just good, great. One thing about CRM professionals, most are very nice people, and generously share what they know. I think this is partly due to their "customer service" mindset and partly due to the "reputation points" mindset that Social CRM encourages. Whatever the cause, I found people happy to share their time and expertise, and it was fun. We all learned a lot.
I noticed three broad themes in this year's conference topics: Social CRM, integration of CRM data into the rest of the company's data stream, and metrics. (Of course, there were other interesting things happening that I missed - after the keynote speeches, there were three tracks going on at once and I had to pick and choose which sessions to attend.)
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Social CRM (loosely defined as Facebook, Twitter, external "enthusiast" forums, and company forums): Once cutting-edge, this facet of CRM has become relatively mainstream. Many "B to C" (Business to Consumer, also "B2C") companies already have extensive Social CRM operations, and others have similar projects in development. For those of you who aren't doing anything like this yet, the overhead of monitoring Twitter and Facebook plus running a simple discussion board is quite low, and the people who mention your company or log into your forum are likely to be more loyal as customers - if you treat them well.
The "B to B" (Business to Business, also "B2B") companies were asking "What about me? What can I do in the Social CRM arena?" I suggested they try to find out where their business customers talk to each other, then go further and find out where and how their business customers' consumers (end-users) discuss things publically, and monitor those. For instance, if you sell a component of Brand X to the manufacturer, you can monitor public social sites for mention of Brand X. Yes, you'll have more brands to monitor, but you don't have to do it in any great depth - just be aware of gossip and firestorms and such. Understanding your customer's business situation can only help with your own B to B sales.
People were also asking about Social CRM metrics. One attendee (I didn't get his name, sorry) said his company did some detailed studies and found that customers who are Facebook "Fans" and forum members are more loyal, more willing to recommend, and purchase more products. So his company was seeing a positive return from their Social CRM efforts from existing customers, before any online marketing efforts to bring in new ones.
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Integration: 20 years ago, integration of CRM data into the rest of the company's computer applications typically took 30 to 80% of an implementation budget. This hasn't changed. As the integration tools have gotten better, the apps have gotten more complex and the integration targets more numerous. The lesson I got was "NIM" - No Immediate Miracles. This is what has always made any implementation hard, and it's still hard. At the Keynote Panel discussion, I asked leaders from Oracle CRM, SAP, Microsoft CRM, and RightNow to work on this - they said they'd get back to me :-)
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Metrics: I've mentioned it in an earlier post, but the importance of Return on Investment (ROI) and other metrics in general was reinforced. Several separate speakers with successful rollouts said that they started by knowing what results they wanted, then built systems (or campaigns, or channels) to try to get those results. The less successful rollouts built systems and then tried to see what metrics were available.
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All in all it was a great show. Many thanks to the organizers, especially Paul Greenberg who was co-chair; in addition to helping many people (including me) with his sage advice, he did many of the tiny thankless tasks that made the event run smoothly.
Wednesday, August 4
Friday, April 23
Everyone wants something different from a CRM system – except your customer.
There are different groups that pull your CRM system in different directions. If you let any of them pull your system out of shape, it won't be much of a CRM system, and it won't really manage customer relationships. It's typically a balancing act between Sales, Marketing, and the Contact Center.
Sales staff are a special breed – relentlessly positive, pushy (in a nice way), and more often than not, far from tech-savvy. They rely on IT pros who will help them use “tech” (including web, mobile devices, computers, etc) to meet their needs. They care about buyers, influencers, decision makers, and want to categorize and rank people accordingly. They want the simplest possible CRM system, preferably one on a handheld device like a Blackberry or iPhone.
Marketing staff are typically also relentlessly positive, and want things right away! A new marketing idea might have much greater value if implemented immediately. Sometimes they have customer contact (often at larger events, not one-to-one). They want to help sell products, and to figure out what marketing messages work, and which do not. Thus Marketing often wants to ask lots of (time-consuming) questions of the customers when they get a chance.
Sales & Marketing management have a different set of challenges – they need data for forecasting and reporting. This data needs to come from the Sales staff, or external data. They want lots of data, but sales staff doesn't want to enter it.
Contact centers are different still – they talk to customers (including end-users) and hear inquiries and complaints. (Some contact centers also take orders directly, or this function may be outsourced. ) The staff wants to efficiently answer the questions, resolve the complaints. But then Marketing often wants them to ask extra questions. And Sales wants the call center to figure out the role of the person calling – is the caller a valid sales lead? The contact center often has other drivers, such as product improvement/surveillance, regulatory, etc.
What do all these groups have in common? Customers. The same customers, usually. A single person frequently sees a marketing message, talks to a sales person, and calls / emails the company. The Marketing, Sales and Contact Center staff typically report to 3 different Directors(!) So getting everyone on the same page is important, and not that easy to do - you need an overall plan to ensure this happens.
This brings up two related points – one on Master Data, one on the “360 Degree View” of the customer.
First, all Account and Contact data need to be centralized and de-duplicated; master customer data needs a single home. Also, processes need to be developed to keep this data authoritative and correct (no one will use the data if it is not substantially correct and constantly improving). An incoming email with a new phone number should propagate to the sales database, a business card collected at a conference should add a Contact record to the appropriate Account. (And try to get lower-cost staff to do the data entry for those, sales staff is expensive.)
Everyone says they want to know all about the customers, and get a “360 Degree View” of their wants and activities, but it’s usually neither practical nor efficient to try to get all the data at once. (If a salesman spent all his time gathering background data on the people he talks to, he’d never sell anything.) So it’s usually best to build this up little by little, and in different detail for different roles. For people who are most influential to sales, more data is worthwhile (including Sales Force Effectiveness type questions about role, personality, “energy type”, drivers, etc.). For auxiliary people at an account, it makes sense to capture less data. As long as there are places to put this info when it arrives, ways can be worked out to get the info into the correct record.
But it all comes back to the customer. It’s called Customer Relationship Management for a reason. To the customer, you are one entity. They don’t care if they are talking to Sales, Marketing, or the Contact Center – they want a good, comprehensive answer, and don’t want to have to repeat themselves over and over. It’s up to you to figure out how… but if you can do this successfully you will be more efficient internally and have better customer service too. I think they call that a “win-win”.
Sales staff are a special breed – relentlessly positive, pushy (in a nice way), and more often than not, far from tech-savvy. They rely on IT pros who will help them use “tech” (including web, mobile devices, computers, etc) to meet their needs. They care about buyers, influencers, decision makers, and want to categorize and rank people accordingly. They want the simplest possible CRM system, preferably one on a handheld device like a Blackberry or iPhone.
Marketing staff are typically also relentlessly positive, and want things right away! A new marketing idea might have much greater value if implemented immediately. Sometimes they have customer contact (often at larger events, not one-to-one). They want to help sell products, and to figure out what marketing messages work, and which do not. Thus Marketing often wants to ask lots of (time-consuming) questions of the customers when they get a chance.
Sales & Marketing management have a different set of challenges – they need data for forecasting and reporting. This data needs to come from the Sales staff, or external data. They want lots of data, but sales staff doesn't want to enter it.
Contact centers are different still – they talk to customers (including end-users) and hear inquiries and complaints. (Some contact centers also take orders directly, or this function may be outsourced. ) The staff wants to efficiently answer the questions, resolve the complaints. But then Marketing often wants them to ask extra questions. And Sales wants the call center to figure out the role of the person calling – is the caller a valid sales lead? The contact center often has other drivers, such as product improvement/surveillance, regulatory, etc.
What do all these groups have in common? Customers. The same customers, usually. A single person frequently sees a marketing message, talks to a sales person, and calls / emails the company. The Marketing, Sales and Contact Center staff typically report to 3 different Directors(!) So getting everyone on the same page is important, and not that easy to do - you need an overall plan to ensure this happens.
This brings up two related points – one on Master Data, one on the “360 Degree View” of the customer.
First, all Account and Contact data need to be centralized and de-duplicated; master customer data needs a single home. Also, processes need to be developed to keep this data authoritative and correct (no one will use the data if it is not substantially correct and constantly improving). An incoming email with a new phone number should propagate to the sales database, a business card collected at a conference should add a Contact record to the appropriate Account. (And try to get lower-cost staff to do the data entry for those, sales staff is expensive.)
Everyone says they want to know all about the customers, and get a “360 Degree View” of their wants and activities, but it’s usually neither practical nor efficient to try to get all the data at once. (If a salesman spent all his time gathering background data on the people he talks to, he’d never sell anything.) So it’s usually best to build this up little by little, and in different detail for different roles. For people who are most influential to sales, more data is worthwhile (including Sales Force Effectiveness type questions about role, personality, “energy type”, drivers, etc.). For auxiliary people at an account, it makes sense to capture less data. As long as there are places to put this info when it arrives, ways can be worked out to get the info into the correct record.
But it all comes back to the customer. It’s called Customer Relationship Management for a reason. To the customer, you are one entity. They don’t care if they are talking to Sales, Marketing, or the Contact Center – they want a good, comprehensive answer, and don’t want to have to repeat themselves over and over. It’s up to you to figure out how… but if you can do this successfully you will be more efficient internally and have better customer service too. I think they call that a “win-win”.
Monday, February 15
GIGO
Garbage In, Garbage Out. (Definition at ZDNet)
Ask any tech guy and they'll tell you that GIGO is one of the most common problems when business line people ask for useful information. You must develop processes and responsibilities to fix bad data. It's too common for users to say "the data is no good, we can't rely on it, so we won't use the system as intended" rather than to work on fixing it. Smart companies are hiring dedicated Data Stewards for their systems - key business decisions are made using this data, and it needs to be as close to perfect as you can get.
The reasons for GIGO issues can usually be broken down into three issues - data entry, data migration from other systems, and system changes over time.
The first is easy enough to grasp; errors are going to happen, your challenge is to minimize it and make it easy to fix. Things like postal code / address validation can help; most countries have postal-code / city mapping data available that lets you enter just the postal code and have the city + state/province populated. Also, turn on spell check for more fields - "Jhonson" is a possible but unlikely last name. And try to make it easy for anyone to fix the data without jumping through hoops, entering a "why did you change this" field, etc.
Data migration is data that comes in from another system, so human eyes don't see it at entry time. It can be a one-time feed at system implementation, or scheduled every day. If the problem is data entry error, fix it in the source system, but it's often more complicated than that. The key is to try to find patterns in the errors and re-do the migration process as needed. There may be a subtle business rule that allows an extra line of address for certain companies, moving every field after that to the wrong column (a real example). Whenever you start doing scheduled data migration, everyone "upstream" on the migration has to know what parts of their data are used "downstream" so changes can properly cascade throughout the business.
System changes are the most interesting challenge. If you migrate from one system to another, the Customer data is likely the same but some core fields like Reason for Contact and Product may have different values. Of course you will want to prepare a mapping document and, after much review by all, get everyone to sign off on it. I'd also suggest keeping a non-migrated version of the data (for the whole record) in a text field (visible or not), so you can see what the record was like in the old system. This can help save you later when someone figures out that the "approved" data mapping was not quite right. If you have the old values stored with each record, they are much easier to find and correct. (Listen to the voice of experience here! Nothing worse than trying to figure out which records are wrong by looking at a system that no one remembers, if it's even installed.)
If you keep the same system for a number of years, dropdown values will change, and what used to be valid data a few years ago won't match the current user interface. The reporting solution may need to have additional (deprecated) values in the dropdown for historical reporting. Also, Management often asks for three years of data when you have been collecting a particular field only one year - your challenge is then to figure out how (or if) that data was collected before and figure out how to report it. It's much better to report absence of data, or explain the report has a large margin of error, than to get it wrong.
Ask any tech guy and they'll tell you that GIGO is one of the most common problems when business line people ask for useful information. You must develop processes and responsibilities to fix bad data. It's too common for users to say "the data is no good, we can't rely on it, so we won't use the system as intended" rather than to work on fixing it. Smart companies are hiring dedicated Data Stewards for their systems - key business decisions are made using this data, and it needs to be as close to perfect as you can get.
The reasons for GIGO issues can usually be broken down into three issues - data entry, data migration from other systems, and system changes over time.
The first is easy enough to grasp; errors are going to happen, your challenge is to minimize it and make it easy to fix. Things like postal code / address validation can help; most countries have postal-code / city mapping data available that lets you enter just the postal code and have the city + state/province populated. Also, turn on spell check for more fields - "Jhonson" is a possible but unlikely last name. And try to make it easy for anyone to fix the data without jumping through hoops, entering a "why did you change this" field, etc.
Data migration is data that comes in from another system, so human eyes don't see it at entry time. It can be a one-time feed at system implementation, or scheduled every day. If the problem is data entry error, fix it in the source system, but it's often more complicated than that. The key is to try to find patterns in the errors and re-do the migration process as needed. There may be a subtle business rule that allows an extra line of address for certain companies, moving every field after that to the wrong column (a real example). Whenever you start doing scheduled data migration, everyone "upstream" on the migration has to know what parts of their data are used "downstream" so changes can properly cascade throughout the business.
System changes are the most interesting challenge. If you migrate from one system to another, the Customer data is likely the same but some core fields like Reason for Contact and Product may have different values. Of course you will want to prepare a mapping document and, after much review by all, get everyone to sign off on it. I'd also suggest keeping a non-migrated version of the data (for the whole record) in a text field (visible or not), so you can see what the record was like in the old system. This can help save you later when someone figures out that the "approved" data mapping was not quite right. If you have the old values stored with each record, they are much easier to find and correct. (Listen to the voice of experience here! Nothing worse than trying to figure out which records are wrong by looking at a system that no one remembers, if it's even installed.)
If you keep the same system for a number of years, dropdown values will change, and what used to be valid data a few years ago won't match the current user interface. The reporting solution may need to have additional (deprecated) values in the dropdown for historical reporting. Also, Management often asks for three years of data when you have been collecting a particular field only one year - your challenge is then to figure out how (or if) that data was collected before and figure out how to report it. It's much better to report absence of data, or explain the report has a large margin of error, than to get it wrong.
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