Power of business analytics to make better business decisions

Benefits of Data-Driven Decision Making with Business Analytics

Companies use Business Analytics (BA) to make data-driven decisions. The insight gained by BA enables these companies to automate and optimize their business processes. In fact, data-driven companies that utilize Business Analytics achieve a competitive advantage because they are able to use the insights to:

  • Conduct data mining (explore data to find new patterns and relationships)
  • Complete statistical analysis and quantitative analysis to explain why certain results occur
  • Test previous decisions using A/B testing and multivariate testing
  • Make use of predictive modeling and predictive analytics to forecast future results

Business Analytics also provides support for companies in the process of making proactive tactical decisions, and BA makes it possible for those companies to automate decision making in order to support real-time responses.

Business analytics techniques break down into two main areas. The first is basic business intelligence. This involves examining historical data to get a sense of how a business department, team or staff member performed at a particular time. This is a mature practice that most enterprises are fairly accomplished at using.

The second area of business analytics involves deeper statistical analysis. This may mean doing predictive analytics by applying statistical algorithms to historical data to make a prediction about the future performance of a product, service or website design change. Or, it could mean using other advanced analytics techniques, like cluster analysis, to group customers based on similarities across several data points. This can be helpful in targeted marketing campaigns.

Business Analytics Best Practices

Adopting and implementing Business Analytics is not something a company can do overnight. But, if a company follows some best practices for Business Analytics, they will get the levels of insight they seek and become more competitive and successful. We list some of the most important best practices for Business Analytics here, though your organization will need to determine which best practices are most fitting for your needs.

  • Know the objective of using Business Analytics. Define your business use case and the goal ahead of time.
  • Define your criteria for success and failure.
  • Select your methodology and be sure you know the data and relevant internal and external factors
  • Validate models using your predefined success and failure criteria

Business Analytics is critical for remaining competitive and achieving success. When you get BA best practices in place and get buy-in from all stakeholders, your organization will benefit from data-driven decision making.

Once the business goal of the analysis is determined, an analysis methodology is selected and data is acquired to support the analysis. Data acquisition often involves extraction from one or more business systems, data cleansing, and integration into a single repository, such as a data warehouse or data mart. The analysis is typically performed against a smaller sample set of data.

Analytics tools range from spreadsheets with statistical functions to complex data mining and predictive modeling applications. As patterns and relationships in the data are uncovered, new questions are asked, and the analytical process iterates until the business goal is met.

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3 Website A/B Testing Tools

A/B testing is becoming more and more common as teams realize how important it is for a website’s success.

[vc_row][vc_column][vc_column_text]The Web is a huge, competitive marketplace with very few (if any) untapped markets, meaning that being successful by offering something unique is rare. Much more common is that you’re competing for the business of your customers with several other websites, so attempting to convert every visitor into a customer or upselling/cross-selling your services better could make all the difference to your bottom line.

Due to this, the market for A/B testing tools and CRO (conversion rate optimization) tools is growing exponentially. But choosing one can be quite a time-consuming challenge, so in this article, I’ll compare the best A/B testing tools to help you decide which is most suitable for you or your team.

A/B testing is about experimenting with visual and content changes to see which results in more conversions.A/B testing often follows usability testing as a means of testing a solution to a flaw in the user experience identified using metrics like bounce rate in an analytics tool like Google Analytics, and thanks to the depth and quality of A/B testing tools available now, A/B testing is accessible to designers as well as marketers and developers.

 

1. Optimizely

Optimizely is one of the leading — if not the leading — A/B testing and CRO tools on the market today. It offers analytics tools to suit users of all levels and a multitude of A/B testing tools. (You could think of it as the Google Analytics of A/B testing, with a much simpler user interface.)

Consider this scenario: You have an eCommerce store built with Magento. You’re aware that in certain cases it may benefit stores to add a one-step checkout solution instead of the standard multi-page checkout, but you’re not sure if your store fits that use case. You need to test both options and compare the results with/without the one-step checkout experience. You know that running two versions of the checkout simultaneously requires changes to the code, which is a complex matter.

With Optimizely, you can send a certain amount of your users to a totally separate checkout experience to collect conversion data. If the experiment yields negative results, you delete the experiment and the original checkout web page still exists and works fine. No harm was done.

With their Web Experimentation tool, which offers an easy-to-use visual editor to create A/B tests without requiring a developer (optional), the ability to target specific user types and segments, and create experiments on any device, Optimizely has all your bases covered.

Although you can run A/B tests without a developer, your variations can be more targeted (for example, your variations can go beyond color, layout and content changes) if you have the skills and/or resources to develop custom experiments with code. By integrating your A/B tests into your code, you can serve different logic and test major changes before pushing them live.

Also, if your product extends beyond the web, Optimizely works with iOS, tvOS and Android apps. Optimizely’s Full Stack integrations make it possible to integrate A/B tests into virtually any codebase, including Python, Java, Ruby, Node, PHP, C#, Swift, and Android.

2. Google Optimize

Google Optimize is a free, easy-to-use tool that integrates directly with your Google Analytics Events and Goals to make A/B testing quick and easy! It’s ideal for traditional A/B testing, focusing on comparing different CTA (call to action) elements, colors, and content.

Developers aren’t required for implementing Google Optimize since it’s as simple as adding a line of JavaScript to your website and then customizing your layout with the visual editor. With this, you can change the content, layout, colors, classes, and HTML of any element within your page.

It’s not as sophisticated as Optimizely, since it doesn’t allow you to create custom experiments with code/developers, but it’s free. It’s great for those starting out with A/B testing. For each Google Optimize experiment, you’ll need to specify which Google Analytics Goals or Events will be the baseline for your A/B tests. For example, if you were A/B testing a product page, you could use an “Add To Basket” event that you’ve defined in Google Analytics to evaluate which of your variations converts the best. The Google Analytics report then gives you a clear indication of which variation converts best. It’s ideal for those on a low budget!

Just don’t get carried away, as Google famously once did, by testing 40 different shades of blue to see which converted best!

3. Unbounce

Unbounce focuses on landing pages and convertible tools. Convertible tools use triggers, scenario-based overlays and sticky bars to A/B test offers and messages to learn when, where and why your visitors convert. An example? If a user tries to leave your site, they’re shown a discount code in a modal or a sticky header, and a test will determine which is more effective.

Landing pages can be an amazing way to validate your ideas, build excitement around a new product, and/or re-engage dormant customers. The problem with them is that they can result in false positives. If you get very few conversions you may feel like your idea is invalidated or demand for the new product doesn’t exist, when in reality users were just unimpressed and/or unconvinced by the landing. Unbounce helps you to determine what your landing is missing.

While you can choose from over 100 responsive templates designed for many markets, goals, and scenarios, and then customize it with your own content using their drag and drop UI, you can also integrate Unbounce with your own design, making a terrific solution for designers and marketers who need to collaborate. Unbounce also works with Zapier and Mailchimp, so data can be transferred across the other apps and tools that marketers use.

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Author: Jamie Murphy
Source: sitepoint.com

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