Product optimization increases your brand’s profile both on and offline by helping your business manufacture and distribute the best products possible. Learn more about product optimization below.
What is the Difference Between Product Optimization and Product Discovery?
Product discovery is the process of determining the minimum viable product possible. A product that is usable, valuable, and feasible to produce and distribute. Discovery is meant to identify additional functionalities within a new product or when contemplating significant changes to an existing one, like a redesign in usability or enchaining the product to target a different audience.
Product optimization, on the other hand, focuses on improving the user experience and the monetary value of your product. Pathwwway product optimization uses separate techniques and tools compared to product discovery. Usually, the scope is much narrower to yield more specific results. Optimization involves a continual cycle of experiments under controlled conditions that sometimes includes your customer base along with web analytics and A/B testing support, commonly known as multivariate or split testing.
Product Optimization Tips to Increase Your Brand’s Profile
Large and mid-sized companies who sell goods rather than services find themselves conducting product optimization almost constantly. Take a look at the tips below to learn how to better your product optimization process from the regular experimentation and user testing to the measurable results and implementation of changes. The more effective and rational your optimization process, the better your products and happier your customers.
Define Your Key Performance Indicators
Key performance indicators (KPIs) define all your product optimization experiments. Useful KPIs vary with your industry and niche. Pick the ones that make the most sense for your business and products. Choose from thousands in different metric categories like sales, marketing, financial, social media, SEO, DevOps, retail, insurance, supply chain, call center, retail, and help desk. Every test you conduct should give you a definitive answer of either yes, it works, or no, it does not work. It even reveals if there is no impact whatsoever.
Change One Thing at a Time
When testing so many variables, try to only experiment on one change at a time. Technically, you could perform product optimization on multiple aspects of the same product at the same time, but it does not alway yield accurate results. Focusing on one change at a time helps you figure out what changes to each individual aspect makes an impact or not.
Find Statistically Significant Results Quicker
The goal should be to run your product optimization experiments quickly to get as much meaningful data as you can in as short a time as possible. You may think that means including tons of people in a testing group immediately, but that would be wrong. You do not want to harm your brand or risk your revenue with a subpar change or a mistake.
Field test the changes in small groups of traffic first to see if everything works correctly. Once this proves successful, increase the test sample to about 50% of incoming traffic. Lots of online calculators exist to help you determine how long to run the testing, usually A/B tests, at a certain traffic volume to guarantee statistically significant results.
Do Not Forget the End Goal
Web analytics go hand in hand with A/B testing tools. Unfortunately, the massive amounts of data produced by the analytics can weigh down the Pathwwway product optimization process by distracting from the meaningful data. Lots of small things happen while someone checks out your website and you cannot forget why you started conducting the investigation in the first place, buying your product, responding to an invitation, etc. The click-thru only counts as pertinent if the clicks lead to your end goal.
Love the Data
Understanding what the different kinds of data are, how they are collected, and what insight they provide your business is indispensable to a successful product optimization process. Making smart judgment calls about what data to measure expedites the whole mechanism based on what will help you intelligently make business decisions. Walk through the measurements and analytics manually until you and your managers completely understand all of the data. Once you have a good grasp on the data and what you need, you can begin automating the reporting process.
Distinguish Between New and Returning Visitors
You would not treat a new customer in your physical store the same as a customer you see every week like clockwork. So why would you treat them the same on your web-based platform? Consumers familiar with your products use your web page differently from new, interested visitors who have never before seen it or your products. They behave differently with distinctive wants and needs. Testing both new and returning visitors during product optimization prevents you from alienating one group or the other and betters your chances of turning strangers into buyers and buyers into loyal customers.
If It Doesn’t Work, Don’t Use It
Sometimes you carefully selected your KPIs and other perimeters, but the results do not reflect any visible differences. This could be the experiment failing due to the change being inadequate, or it could be the test itself. If you notice a test does not provide relevant, valuable data do not bother launching it. You are better off using an effective test that keeps your site clean and fast-loading.
Beware Diminishing Returns
At some point during the experimentation, you’ll reach a point of diminishing returns at the local maximum. Progressing past this point requires substantial changes to your experiment perimeters or, perhaps, even trying another hand at product discovery. Although one of the above rules stated you should only test one change at a time, when you reach this point, you may benefit from increasing that number to two or more.
Avoid Common Problems
Finally, the best way to improve your product optimization process is to learn common problems that occur during the natural course of experimentation and stop them from happening at all. Ensure the responsiveness of your testing reflects how the product would operate in reality. Sometimes experiments must run for a week or more to account for those long-term trends when you could get sufficient information after a day or two. Try running A/A test continuously to avoid automated robots pinging your site and disrupting your data.