Measuring sentiment?
Often as marketers and analytics professionals, we are striving to measure every dollar. Yet, it may be equally important to get a read on things prior to the dollar figures. What about sentiment? Facebook knows. Google is learning. Pandora built a business around it. What about your org?
Sometimes we need to measure things faster and the dollars are more grey than black or white. Our advice? Improvise. Ask people what they think in 1 click. Yes or No? Like or dislike? Up or Down?
How is your organization leveraging sentiment to improve your marketing cycle?
SAS Altlog option
If you run a production SAS environment or even a development environment for that matter, it is nice to have a specific location for all logs to be written and managed. ALTLOG helps achieve this.
The ALTLOG option can be set numerous ways, but we typically utilize 2.
1. As an option on the command line. Example:
sas sasprogram -altlog /my/log/dir/sasprogram_$timstamp\.log
2. Globally in the sas configuration file. This is wonderful as an administrator. All of the logs drop into one location for easy cleanup or archival. Users can specify an alternate location if they don't want it in the default, so everyone wins.
Improve engagement with simple analytics, I mean dynamic content..
When we use words like analytics or algorithms, marketers eyes glass over. However, when we use words like dynamic content and positioning, they get very excited.
In reality, we may be talking about the same thing and it isn't brain surgery.
So, from a marketing / technology / analytics point of view, here are a couple of simple steps to improving engagement:
1. Build media outlets / channels (think website, mobile app, LCD touchscreen in your lobby) so that you can know who is interacting and possibly why (what do they want to know?).
2. Arrange content into categories such that they align with the who and why from step 1. This will make life much less complicated. Also note, content may be duplicated between categories.
Example:
Channel / Who / Why
- Primary Website
- Gold user in US
- Price shopping
- Main image1
- X-sell1
- X-sell2
- Store locatorMain image2
- X-sell1
- X-sell3
- Price shopping
- Gold user in US
3. Come up with a measurement strategy. An easy way to keep measurement simple is choose an engagement metric. This will be one number or metric that will tell you if users are interested in your content (maybe clicks, data entry, or downloads)
NOTE: Even on the best channels, not all content spots are going to engage users. Make sure that you are maximizing engagement when and where the users desires.
Most of all KEEP IT SIMPLE. This is the 'walk before you run' approach to dynamic content and analytics. The next level includes more words like algorithms.
SQL tip – Inner join shorthand with USING
We write a LOT of SQL here and although SQL is a powerful database language, it can be tedious. So here is one tip for shortening all that typing.
Typical join sytax:
select some_columns
from one_table join another_table on one_table.column_1 = another_table.column_1 and one_table.column_2=another_table.column_2
where some_column > someother_column ;
Not too bad, right?
In order for this tip to work, you must be joining on columns with the same name (ie. column_1 and column_2 have the same name in both tables).
Here it is:
select some_columns
from one_table join another_table using (column_1, column_2)
where some_column > someother_column ;
Now, in our example it's not a drastic difference in coding, but as any programmer knows, this shorthand example doesn't accurately represent the real world. So give this syntax a try on your next project and let us know if it doesn't save you some typing.
Developing an effective contact strategy
There are few things that seperate the good marketers from the bad. In our opinion contact strategy is one of those things that few organizations understand and all should consider. When you are trying to decide who you should contact, through what channels and how many times, here are a couple of things to think about.
1. Not all customers are created equal - Nor do they have the same appetite for your marketing efforts. We recommend you spend time looking at your customers and how many times you contacted them in the past. A good cluster analysis will begin to help you understand that some groups prefer 10 'sale' messages and 2 'partner' messages before opting out. Put this cluster with other customer breakouts and you have the basis for your contact strategy.
2. Not all messages are created equal - As we eluded to above, you can not simply look at the recency and frequency of your messages to determine how much is too much. You need to understand the message types or objectives. Customers have varying appetites for your credit card solicitations and that appetite is much different from your best customer sales messages.
3. Implementing your strategy may be harder than developing it - So now you know what you want to implement. Customer segment X gets Y transactional messages a month and Z surveys a month. But how do you insure that they only get what you've prescribed? There are a variety of ways to accomplish this with either process changes and / or technology solutions (One of our favorites is Unica's Optimize.).
If you are considering a contact strategy for your organization, we would love to help you understand your customers and improve your communications.
SAS / Teradata Fastexport – dbsliceparm = all
Fastexport is the fastest way to get large data out of teradata. Fastexport utilizes multiple connections to deliver data and therefore speeding up the transfer of data between Teradata and SAS.
Here are a few examples of fastexport.
/* libname statement*/
libname teradb teradata username=&un password=&pw dbsliceparm=all;
/* explicit sql */
proc sql;
connect to teradata(username=&un password=&pw dbsliceparm=all);
<SQL STATMENT>
quit;run;
How do you know if fastexport was used?
Use this option:
options sastrace=',,,d' sastraceloc=saslog;
If it is working, you should see something in your log like:
Select was processed with fastexport.
There are many other factors that come into play if fastexport doesn't work. Check the requirements on the SAS support page for troubleshooting.
Analyzing Paid/Owned/Earned Marketing
As more is written about various categories of marketing, we can't help but think analytically about the implications of approaching marketing problems given their categorical differences. Of late, much has been written about 3 specific communication or marketing types:
- Paid media
- Owned media
- Earned media
This breakout is logical, however any marketing breakout must be based on some common public thinking about 'channels'. Thus customers must interact and respond differently to those categories. During some of our most recent analysis, we found that applying some of our traditional segmentation matrices to the response indicators for these categories told a specific story for one client. Various customer types respond distinctively different and to varying degrees to each type of marketing.
Now this might not be ground breaking on it's appearance, but if an organization could understand it's customer base to the point of actioning it's marketing toward the most profitable segments within each category, therefore maximizing it's return in paid, owned, and earned, great efficiencies could be made. It would be apparent which channels need the most focus and are the most profitable for an organization.
In the case of owned or earned media, organizations could save large portions of marketing dollars from being wasted in the paid category by maximizing 'word of mouth'.
Yet I digress. All this to say, understanding one's customers and the preferred and most profitable interactions of those customers is what creates efficient and effective marketing. Paid, owned and earned is no Earth shattering new marketing breakout, but if this is how marketing thinks about it's channels, it is how analytics should organize to help them understand where their attention should be focused.
4 tips for integrating CRM technology
If you have worked with marketing technologies for any length of time, you've encountered this problem. How can we integrate our CRM with the rest of our organization? How does CRM interact with our call center, website, applications, database, loyalty system, etc., etc.
We've been there.
Throughout our travels, we've picked up a few tips. Hopefully they'll help you.
- Tight integration is dangerous. Just don't do it. - Marketing technology changes faster than fashion. Today's latest and greatest is quickly replaced with tomorrow's new market maker.
- Integrate through marketing concepts not technical application specs. - Abstracting technologies through the use marketing concepts like 'campaign', 'offer', 'contact history', 'registration', 'enrollment', etc. Define what these mean for the organization and build a data structure flexible enough for all applications to contribute to those concepts.
- Databases and services are the lifeline. - Long term success hinges on ease of use and flexibility. Simple services allow for plug and play scenarios. Well designed databases create environments that record interrelated transactions based on their role or concept. This makes business people happy. They can measure, analyze and predict without spending precious hours compiling, cleaning, and organizing data.
- Think replaceable. - Always remember that what you are adding to your technology stack could be replaced in a year and you will need to re-integrate something else. How much of your current work will be thrown away? Minimize the one-time development and maximize your efficiency and cost savings to the organization over the long haul.
We like to think we aren't integrating technology. We are simply making all of our applications play nicely together.
There is much more to this topic, but these 4 tips can minimize your stress for ears to come.
SAS: Where Also
Ever heard of 'where also'? Neither did we.
We have to give credit to the guys at the SAS Community.
'Where also' allows you to add a series of where statements. The use acts like a single where statement with the and condition.
Example:
Data new_data;
set old_data;
where number=6;
where also another_number=.;
run;
This is the same as where number=6 and another_number=.
Pivot tables and Cheezy Graphs? No more…
As any good analyst knows, working with data is not the problem. Good analysts have the skill necessary to combine, split, merge, slice, aggregate, or summarize data any way you can imagine. However, is that enough?
Probably not..
What good does data do if you can't communicate its meaning in a logical way. The day of sending a spreadsheet with pivot tables are heading out the door. Today users are wanting more. They are expecting more. And why is that?
Here are a few reasons users expect more from their data experiences:
- Google Analytics
- IPhone
- ITunes
- Amazon
These are just some of the companies leading the way in user interface. The days of cheezy HTML or excel graphs won't cut it. Users quickly see they have to work to understand the information and move on.
The solution?
A complete toolbox of data tools and a team of professionals with tool experience. Today's environment requires (along with some of our favorite vendors/partners):
- a storage solution
- an analysis tool
- a display layer
- Data Applied
- Custom solutions (Juice Analytics)
If your organization isn't firing on all analytic cylinders, let us know. We have the tools, relationships, and people to move you to the next level. We have a proven track record of moving the needle through the integration of data into decision making.