Praxis Metrics- Data Predictions

Predicting data trends (2018-2020)

For this blog post, we wanted to revisit a guest episode that we did with the Vision Tech Team. This episode was filmed at the end of 2018, and focused on the changes that we saw in the data landscape in 2018, as well as predictions for 2019.

We found this episode particularly relevant given the changes in the data landscape that we detailed in last week’s post (which you can find here).

Enjoy this short podcast episode, and our insights below.

Insights from 2018:

  1. The data landscape changed rapidly, making it very difficult to predict what different platforms would do.
    1. Infusionsoft became much more open with their data in 2018, while Facebook began to clamp down on what data they would allow access to.
  2. Businesses began to understand the value of data, and started to understand it’s importance.
    1. Similar to the dot-com boom, businesses that fail to take advantage of their data started falling behind in the market.
  3. Data overload started coming to a head. Businesses started needing to figure out what data actually drives results.
    1. Trust in data became an issue. Data validation became a necessary part of data projects.
  4. Data visualization became the new buzzword.
    1. Everyone became obsessed with visualizing their data.
    2. This often came at the expense of actually driving new insights, as they would simply slap graphs on the same data.
  5. Automation started to spread
    1. In the beginning, nobody tracked anything.
    2. Then, businesses started tracking, but the data was stuck in silos.
    3. From there, data nerds started creating complex pivot tables and Excel sheets to bring the data together.
    4. Now, automation became feasible, and started taking over. This removed the need for manual reporting, and made the visualizations better.
  6. Going back to data overload, tracking exploded in 2018.
    1. You started being able to track anything that you wanted, and this lead many companies to overload on data.
    2. For those that actually leveraged this data, it caused explosive growth.
  7. Chatbots exploded onto the scene.
    1. Chatbots began to expand their footprint, with very promising results.
  8. Businesses began realizing the value of data democratization.
    1. Rather than having just one data nerd knowing everything about your data, businesses began to share data with all employees.
    2. By opening up data to the entire organization, you can gain insights that you previously would have missed out on.
    3. This allows organizations to leverage the combined brain-power of their entire team, rather than just the select few.

Predictions for 2019:

  1. Increased competition for data.
    1. As more companies realize the value of data, more and more companies will start competing for it.
    2. As data becomes more ubiquitous, companies will get better and better at targeting and marketing. This will drive inefficient organizations into the ground, leaving only the best to survive.
  2. Data overload to the max.
    1. As more data becomes available, businesses will need to start deciding what metrics actually matter to them.
    2. Most businesses will need to hone in on the handful of metrics that actually drive results (80/20).
      1. Those that capitalize on this principle will thrive in 2019
  3. Increased transparency and accountability.
    1. As more companies move towards data democratization, we will see a shift towards increased accountability across organizations. This will create cultures that thrive on extreme accountability.
    2. Since everyone has access to the data, it will become much more difficult for low performers to hide behind the work of others.

Looking back on 2018, things seem so much simpler. They only worried about compliance with GDPR. In 2019, we have a host of regulations on the horizon to worry about, decreased access to data and information, and platforms throttling data.

Looking forward to 2020, we’re likely to see an acceleration of the changes from 2019. We expect more data regulations to come into effect, and as a result of this, we expect a decrease in marketing efficiency.

One of the things that every business should start doing is gathering data into a data warehouse. This protects you from the whims of the larger platforms, and gives you complete ownership over your data. By gathering your knowledge and wisdom into your own database, you insulate yourself from the storm that is brewing on the horizon.

Praxis specializes in data, so we can help you take ownership over your data, create back-ups, and help you understand your data better than ever before. You can learn more about our products and offerings here. If you have immediate questions, you can contact a data expert directly here.

Praxis Metrics- Chatbot KPI's

4 Fantastic KPI’s that you should be using in your chatbot marketing

How do I measure the effectiveness of my chatbot marketing?

We decided to have a “chat” with the king of Facebook marketing, Curt Maly, to get his thoughts.

Curt is a social marketing expert, owner of multiple online marketing businesses, consultant and national speaker joins us today with his vast knowledge of all things marketing data related.

What are the benefits of chatbots?

People generally react faster to the notifications from a Facebook message than to notifications about an email. Not every customer responds positively though, most people tend to the extremes in their feelings towards these messages: they either love them or they hate them.

Another benefit of using chatbots in marketing is that they help to automate out customer service. Chatbots essentially create auto-responders within an AI platform, allowing you to dedicate your time and resources elsewhere.

Chatbots also help decrease the necessity of traditional funnels. You can utilize chatbots to move your clients from one point of the traditional funnel to the next without needing to set up traditional stages. Rather than segmenting people into phases like a traditional funnel, chatbots allow you to just have a conversation with the client and naturally progress them through their journey.

How hard is it to set up chatbots?

Just like funnels, you can make chatbots as simple or as complex as you would like. Creating a basic chatbot requires minimal knowledge, and only needs a few options for auto-response. Essentially you just need to think through a standard conversation that happens on a sales call or customer service call, and input the different variables into the chatbot.

KPI #1- Cost per Acquisition

This is the amount of money that it takes for someone to engage with your message. You can find this information by dividing your ad spend across the total number of new messages that you get. Facebook will try to conflate this data with Cost per Reply, but that will give you an inaccurate picture of your actual cost per acquisition.

KPI #2- Cost per Reply

Facebook will track this metric automatically for you. It’s a very important metric to keep top of mind, because it can help you understand the cost of re-engaging a lead that you fell out of contact with.

KPI #3- Cost per Open

This metric matches up nicely to open rates on email, with one difference: most people see at least an 80% “open rate” on Facebook messages. This happens because if someone browses Facebook on their browser, the message will automatically open, inflating the open rate.

KPI #4- Click Through Rate

Click through rates on Facebook messenger generally align very closely with email click through rates. Most people will see between 6-10% click through rates on Facebook messages. Many people mistakenly claim that they get better click through rates through Facebook messenger. In reality, they are generally getting more leads than from email, due to the increased open rate of Facebook messages.