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.