Why Use Google Adwords to Get Started on Marketing

I run a startup called TrainingMetrix, a company dedicated to bringing accessible analytics to fitness users around the world.

With a beta release of our first online product for triathletes, we needed to start a marketing campaign or two to draw in traffic beyond the inner circle of developers and “friend” based beta testers.   We aren’t fans of Facebook, so using a Facebook Ad campaign was not an option.

Google Adwords was a suggestion from a friend of mine.  As it turns out, Adwords is a great platform to get started on.  You can set a budget, big or small, you can run one or many campaigns for as long as you want and the text “adwords” means you don’t have to have a graphic artist and fancy banner ads to attract customers.  For us, the control, flexibility, and simplistic nature was why we chose Adwords for our first marketing platform.

So, we signed up with Google Adwords and started experimenting.  We had two goals for this project:

  1. Test the waters with different keywords, messaging, and landing page styles to understand trigger words
  2. How effective Adwords are in terms of cost

We setup four different campaigns, each with two separate Ads and sat back.  Before too long, we were getting plenty of traffic to the site.  We let the campaigns run for one week and then ran a report.  The results were fascinating to us and exactly what we were looking for.  Our first goal was met.

As for our second goal, the cost of Adwords was within our budget, but the overall cost per acquisition (CPA) was quite high, higher than we expected.  If we had Lifetime Value data to compare against, we could make a smart decision as to whether or not to continue on.  Since we are in the beta phase and not collecting revenue, the overall costs of the campaigns were acceptable, on budget and appropriate for the knowledge that we gained.

If you have a startup, please consider using Adwords as a jumping off platform for marketing. Leverage the simplicity, the control and the knowledge that you can get from it.

What Makes a KPI Special

A key performance indicator (KPI) is a vital tool for an organization seeking grow smart profits, expand customer loyalty, and build a scalable workforce.  While many companies stick a chronological set of numbers on a chart to project it on a wall in front of stakeholders, the reality is what they are seeing may not be a true KPI.   Its just a number unless its special.

To be special, the number needs to have most of the following characteristics in common:

  1. Represents a hierarchy either by time, or by dimension
    1.  Example: Sales broken by Year, Quarter, Month, Week or even day, then sliced by Sales Region
  2. Directly actionable with each stakeholder holding a piece of the action.
    1. Example: Sales are down 15% from goal, Sales Managers, Marketing Coordinators, etc can all speak to how their actions influenced the number
  3. Has  common definition across departments throughout the company
    1. Example: A sale is an action by a user from a variety of channels and promotions that resulted in payment and excludes affiliates for instance.  This definition is signed off by Marketing, Customer Service, Sales, and Product.
  4. Does not include hidden meaning and/or does not hide a trend
    1. Example: A number compared year over year, month over month completely ignores a trend that can be alarming.  While sales could be up year over year, this metric could hide a sudden drop in sales from the beginning of the year.
  5. When displayed with other metrics on a dashboard, the number represents a part of the story and does not represent a conflict to other metrics.
    1. Example: A number is where a chart shows Sales sky-rocketing, but another chart shows New Revenue way down, and yet another charts show Average Deal Size (ADS) flat.  The three charts give conflicting information, so one or more can’t be a true KPI for this business.  In fact, in this case, Sales is the corrupted KPI as it does not conform to a standardized definition.
  6. Supporting data is transparent
    1. Example: Analysts should be able to review aggregate data that rolls up to the KPI for audit reasons.  It provides transparency and allows for drill down capabilities. Often the Analysts receive direct support from a data team are sourced from data sources throughout the business.
  7. The number evolves with the business
    1. Example: A number is just a number unless it can change readily with the business. A team supports the KPI, new data is added when created, and definitions evolve as the understanding of the business evolves.
  8. A goal can easily be set and tracked from the number
    1. Example: If the number is fully understood, a goal can be set and tracked against.  If you can’t answer, “Where do we need to be by the end of the Year?”, then its a number, not a KPI.

Throughout my career, I’ve seen numbers and I’ve seen KPI’s.  The most successful companies I worked with not only have a data team to support their KPIs, but they engage in regular discussions of the KPIs at all levels of the business.  Successful, scalable, and profitable businesses are the ones using special numbers at their core.

When was the last time you saw a number masquerading as a KPI?

Why Marketing Analytics Is More Than Just A Coversion Rate

When I first ventured into the Marketing Analytics realm so many years ago, analytics were simple.  All we needed to know was how many visitors made it our site and from where, and then how many of those converted to trials and sales.  You can easily satisfy marketing stakeholders by slicing these conversion rates into their area of focus, be it Affliliate, Online, Email, or Offline to name a few.

But, over the years since I have to say that Marketing Analytics have evolved into quite a profound and somewhat complicated science that is even more fascinating.  As time passed and companies struggled to control Customer Acquisition Costs (CAC) and Marketing budgets got slashed at the same time, Marketing execs found themselves having to dig deeper for a few reasons.

First they had to justify their current CAC by tapping into the Finance metric of Customer Lifetime Value (CLTV).  They then had to dive into cancellations to understand Drop Rate to see how many of their new customers were “sticky” versus “loose” (we called these net zero customers, who purchase and leave in the first month).   It used to be that Revenue was key, but many Marketers have learned that Revenue metrics are slow to respond to changes in the acquisition funnel.  Hence the need for Drop Rate and CLTV by acquisition to compliment conversion rates.  But then a fundamental shift in marketing came just a couple years ago.

Social media is the latest marketing fad.  The most difficult thing about this fad is the lack of measurement.  Facebook “likes”, Twitter followers, mentions and wall updates are extremely difficult to translate into a monetary return on investment (ROI).   Successful companies have invested a lot of into creating and maintaining their brand, which pulls money away from more traditional and easily measured channels.  While cancellations and CLTV are not directly impacted here, the health of a social media campaign can only be judged by how much it enhances the brand.  Along side our conversion rates, we see “interaction metrics, such as responses to tweet and wall updates.  You see, if your social media guru is posting stuff that your customers do not comment on, your guru is not a guru.

Not only do marketers  have to know if your customers interact on Twitter, but they also need to know how their customers use their products.  So, marketing should have readily available metrics from the CRM/product/content teams such as % usage rates, % support calls, as well as product personas.   If your company uses the Net Promoter Score, heck, marketing should have access as well.

What does Marketing Analytics look like today?  Well, those conversion rates are enhanced by post acquisition metrics.  However, it isn’t as easy as it seems.  In order to provide marketing with the enhanced data sets they need to compete in today’s corporate world, they need the support of Business Intelligence & Web Development teams to tie everything together.  There is nothing worse than having a great product and not knowing anything about your customers because no one ever thought to implement unique customer tracking on the website.

While Marketing Analytics today are a bit more complicated compared to a few years ago, it is a fascinating place to be.  Marketing is one of the few departments that really need a global view of the company, the product, and the customer to succeed.  As an Analyst, this viewpoint is a goldmine for data geekery.

When wast the last time your Marketing team looked beyond conversion rates?

Data Perspectives – Trial Users

One of the keys to getting customers hooked on your SaaS product is offering a free trial.  Letting someone experience your product for free for seven to thirty days is a great way to establish trust with the potential customer, let them experience the product, and also gain insight into how they will use the product (customer segmentation).

On a recent project, I was reviewing data for a client and noticed a very interesting pattern in the login histories (not really, but we will call it logins since the real data can’t be shared) for trial users.  This particular client offered a 7-day credit card trial with auto convert to a selected plan (i.e. monthly or annual).    What I expected was a nice curve from day 1, declining each day, relatively smoothly and then an increase in logins after conversion.

However, after summarizing the login data for the first ten days of service (including 3 days for the auto convert), I found a sharp decrease in logins from day 1 and day 2, as well as a blip on day 6. See the chart below.

What was even more fascinating is how the other analysts and “experts” at the company interpreted this data.  Some of the comments are below:

  • “Wow, people pay us and use less?” – referring to the drop is usage on day 8 after becoming a paying customer
  • “Those auto convert reminder emails are working, driving usage!” – referring to the increase in logins one day prior to trial end on day 6
  • “Looks like we need pay per login” – referring to the sharp decline in logins from day 1 to day 2
  • “If we can get the customer to use beyond day 4, we have them!” – not sure how this really fits in as we haven’t correlated logins with LTV, yet
  • “People are cheap” – referring to the people logging in on day 6 to use the product prior to cancellation
  • “If you are going to login to cancel your auto convert on day 6, wouldn’t you try the product one last time?” – again, referring to day 6

The chart is quite simple, a single line with 10 data points.  What isn’t simple is really what this data means.   In fact, I don’t think we can make a decision directly from this data. Rather we need to further understand what the trial users are actually doing on day 6 and how users with logins on day 7 compare to the users on day 1 (is this a bad a marketing channel).  It would also be great to dive into patterns of logins just prior to churn or trial cancellation.

What fascinates me the most, is not only the different perspectives on the data, but the deeper questions that come out of the data.  Data and customer insights are evolutionary.  The more you know, the more you ask questions and the more the decisions and knowledge evolve.

Working With Data

Data is the future.  The future will continue to see an explosion of data collection and an increasing need to digest it.  This is what the industry refers to as “big data.”   The ability of one company to collect, analyze and take action on large amounts of data can be a serious game changer in the marketplace.

Any stakeholder who seeks to be successful in their role will leverage data.  Given the imperfections of our world, the stakeholder may have access to a limited data set.  While the stakeholder recognizes their need for clean, accessible data, the IT and BI teams may be months away from delivering.

The stakeholder has has two choices: 1) throw up their arms, complain about the data and cause a ruckus, or 2) work with the data they have and make the best of the situation.

Throwing Up Their Arms

“The data is wrong!” yells the marketing analyst sitting in a meeting with IT and BI teams.   The IT and BI managers shrug their shoulders and reply, “then tell us what is right.”   The marketing analyst bangs her fist on the conference table in frustration.

Bottom line, stakeholders who don’t embrace even the worst data, does not understand how to measure their business.  I’ve seen exchanges between BI and stakeholders where data has been subjected to strict QA by the stakeholder, but the stakeholder has never referred to the data as wrong.

Work With the Data

Every stakeholder interested in a data set needs to have the long term picture of the business in mind and understand the KPIs and other metrics involved to manage their part of the business.  All data used in analysis are typically seen through the lens of the business KPI which provides the context.  Chances are a stakeholder would never accept a data set that is so far from the truth to be useless.

Based on my career, the best course of action is to work with the data you have.  Granted you might not be able to answer more complex business questions, but you will start a journey along a road that will get you there.  Take the data you have and create three lists:

  1. parts of the data set that works for your requirements
  2. parts of the data set that should be modified
  3. parts of the data set that are important, but not pertinent to the requirements

Your goal is to understand the ins and outs of the data you have and create a constructive list of actions that evolve your knowledge and the data set into a market changing analysis. Providing documentation on to help the IT and BI teams evolve your data and turn into your pot of gold is the best course of action.

Data is Not Static and Neither is Your Knowledge

Keep in mind that as you interact with data, ask questions, build more detailed documentation and draw correlations or disassociations, your data will have to change to follow your in-depth understanding. This is why maintaining a positive relationship with the team that you rely on is so important.

Iterations of data sets can be subtle and they can also be large.  Just remember, that the data you had for version 1 is NOT wrong compared to version 2.  When reflecting back on version 1, understand where you came from and that you are looking at a less evolved set of data.  Then you can laugh when you look at version 3 and wonder how you managed the business with version 1.

Working with data is an awesome thing.  It should be a fun, productive journey for both the analyst, IT team, BI team, and all stakeholders involved.   When you here the word “wrong” come up, defend the evolution of data and point out that perfect data sets don’t come out of thin air.

What Do You Know When You Don’t?

Knowing something is half the battle. Communicating that something is the other half, but that is the subject of another post.

Knowing how your customers use your product and how they want to interact with you is a great thing. Two major steps toward becoming customer oriented.

But then you realize that what you know, might not be what you know. You see signs that your data may be incomplete or inaccurately measured. What do you know now?

1) You need to stop and reevaluate what you know.
2) Depending on the issue, you might be realizing that what you know, might be valid to some degree.
3) On the other-hand, you might be realizing that an entire rebuild of the data warehouse is in order.
4) You start making a mind map of the situation to visualize the scope of the problem.
5) You start counting the knowns, the unknowns, the could-be’s and the what-if’s.
6) You then start to think that maybe if you ignore it, it might, with a one in a trillion change, actually fix itself.

Then you realize you just reconstructed the famous Donald Rumsfeld speech:
“There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don’t know. But there are also unknown unknowns. There are things we don’t know we don’t know.” – Donald Rumsfeld (more Rumsfeld quotes)

In the end you may know much more than when you started. But the process of determining what you don’t know and what you do know is a great one! It is called validation.

Everyone should have a process of validation.

Confidence In Metrics

Metrics are the individual numbers that relate to other numbers that tell a story. Often, these are the numbers that people of all levels of the company use to make decisions.

If a company has a firm grasp of their metrics, fully understand their engine, and have reporting in the right places, there can be some pretty impressive looking dashboards available that tell the health of business at a glance.

However, those fabulous looking metrics and dashboards might be completely meaningless if the there is a broken link between the customer touch point and the data server.  As companies grow, data grows exponentially and marketing landing pages pile up on the server, there is more and more potential for something to break.  Undetected breakage can kill a company!

The best way to resolve this is to assume that the entire system is broken and one must test, test, and test again.  Keeping records at the major data transfer points is one way to keep track of records dropping out and why.  Doing routine quality checks on the data is also a way to keep confidence levels up.

Knowing the data, the trends, and the engine is a great way to detect breakage.  If sales from search take a dive and they have been consistent for the past three years, maybe something broke… maybe your campaigns need a refresh.  A good analyst usually has great instinct on the issues.

Patience in drilling down, slicing and dicing, and becoming intiment with your data is the only way to understand it, make sure it works, and that the story is non-fiction as opposed to some strange, poorly written fiction novel.

How confident are you in your metrics? Is it fiction or non-fiction?