The intersection of tech, marketing and analytics are where business success happens.
Emerging technologies, like AI and cloud economics, are driving customer data analytics capabilities and changing the marketing landscape. A customer journey can be tracked online, offline, and in-app, so web analytics alone fall short. Web analytics track site speed, visits, and load rate. Customer data analytics track people.
In the expanding market for content, data, and algorithms, new and emerging companies are delivering next-generation tools that automatically connect every touch point to a user for a holistic profile.
Data is our key to understanding how customers engage via mobile, social, email, and chat.
Of course, all this data needs to be tested.
Scientific methodologies exist to help marketers set up and compare different campaigns to find out what works best.
Experimentation through A/B testing, multivariate testing, and AI-powered algorithms can help you optimize your campaigns and better connect with your targeted user segments.
Testing employs a set of trials and related analysis to determine which version of an ad, app, email, design, or web page has higher success toward a stated goal. The goal might be beta users, product purchases, email subscribers, or followers. It is beneficial for you to spend the time to understand what factors persuade a decision to take action or to bounce.
What are the Steps in Scientific Testing?
There are seven steps to launch and execute a successful test.
You don’t need to be a data scientist to run scientific experimentation. In fact, marketing itself is a continuous experiment. It involves testing, learning, and optimization.
- Perform Analytics
- Define a Goal
- Create a Hypothesis
- Establish a Control
- Determine Variable(s)
- Run Trials
- Measure Results
Working with a Control in A/B Testing
The Control, in testing, is the original ad or design which is to remain unchanged for comparative purposes. Everything stays the same in the Control so that you can test against it.
Working with a Variable in A/B Testing
The Variable, in A/B testing, is the alternate version of the ad or design. This is where you make changes, or a series of changes, for the purpose of comparison to the Control.
Establishing a Dependent in A/B Testing
A user’s response to an ad depends on the elements of an ad, hence the term dependent. If the messaging, visual communication and value proposition of an ad is compelling then the user may click-through or subscribe. The dependent, of course, is related to the business objective set for the ad campaign, such as increasing clickthrough rates, to increase revenues. Make sure your goals are set up on Google.
Examples of dependents in A/B Testing
- Email subscription
- Contact form completion
- Clicks to social accounts (like Facebook, Twitter, Google+, and Pinterest)
- Engagement goals like time on site or pages per visit
- Watch product video
- Product or trial version sign-up
- Engage with chatbot
Measuring Performance in Testing
Measuring marketing results allows you to assess how you are doing against your objectives. A metric becomes a KPI only when measuring something connected to a goal.
Example: Our flower store’s business objective is to sell bouquets. Our KPI could be the number of bouquets sold online.
This is the reason why you need to have your business objectives clearly defined — without them, you will be unable to identify your KPI’s. If you have proper KPIs and look at them periodically, you will keep your strategy on track.
Best Practices for Testing
Creating a culture of experimentation is the best way to hone in on optimal brand marketing and campaign variables.
Make sure you’re talking to your marketing team. If there are cohesion and buy-in across teams then it is easier to reach the audience and meet goals.
- Create a firm-wide culture of testing
- Make the connection between technology, design, and marketing teams
- Share your data and testing across teams
- Structure your experiments so that you can reach a valid conclusion
- Run your tests long enough to collect sufficient data
Trends in How Organizations Optimize
AI-powered marketing tools are giving companies a competitive edge in conversion optimization testing.
Evolutionary algorithms and automated multivariate testing enable experimentation with multiple changes to a campaign. Statistical techniques are being employed to predict the performance of the winning design from hundreds (even millions ) of design combinations. Yes, millions.
AB trials and multivariate testing tools, combined with analytics tools are still being used to help companies drive engagement and optimize conversion rates. But now, companies are using Artificial Intelligence to predict winning designs and to tailor brand messaging to their customer base at every touch point. As data collection increases, and experimentation along with it, testing has evolved too.
The result? Firms can optimize designs quickly and continuously.
Today there are many firms focused on continuous optimization through quick and simple campaign element substitution, while a number of other firms seek to replace classic A/B testing with AI-powered automation.
The evolution of testing means that we can add additional complexity to our experimentation. Instead of a series of single, AB decisions, platforms can perform continuous sequential marketing decisions.
Tools make it easier to experiment, gain valuable customer insights, and measure impact that changes have on your metrics.
Whether you’re a large company looking to implement sophisticated personalization and map complex user journeys, or a start-up looking for basic landing page testing, there are many tools out there to help.
Here are just a few.
Visual Website Optimizer
VWO is an all-features-in-one optimization platform that helps you organize your goals, create a hypothesis, and adjust your website at scale in response to how your visitors are navigating it.
Optimove’s built-in marketing optimization bot bridges the gap between data science and the art of marketing. By autonomously optimizing campaign performance and discovering performance-boosting opportunities hiding in the data, Optibot helps marketers maximize the impact of their efforts.
Sentient Ascend is an amazing platform for autonomous and constant optimization. Their artificial intelligence lets you try out more design ideas in less time and requires less traffic than A/B and multivariate testing.
Named a leader in Gartner’s Magic Quadrant for Personalized Engines, Dynamic Yield is a go-to solution for institutions. Their platform runs predictive tests and machine learning optimization across web and mobile. The analyze, test, optimize sequence is continuous for an ongoing read on users’ needs.
Google Web Optimizer
Google offers a simple but free A/B and Multivariate testing tool that lets you get the job done on a budget. And if you want to conduct content or app experiments Google has Content Experiment and Experiments API.
Originally published at weformulate.io.