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Marketing Automation in 2016

Target ROAS bidding for improved marketing automation

We’re there. It’s been over three years in concept, research and development. Business technology and marketing automation are changing a lot. We now have more advanced access and easier use of API with web hooks than ever before. It’s an incredible feat because a year ago we were barely able to connect some software and services. Now API are native to practically every popular application for business. The truth is this, even with all these incredible hooks and features, marketing wont be entirely automated any time really soon. What we can do however is exchange more data and information then utilize machine learning and self-awareness supported by input and feedback. So what exactly does this look like and what does it mean for companies? Well using data such as projected/estimated value on new business opportunities. For example, what if we could map your new opportunities precisely back to their sources of influence and amplify those. That’s exactly what machine learning is capable of. We adopted the use of Google’s target ROAS (return on ad spend) bidding in 2013 and have been working with it and our products to provide that type of optimal value to our customers. In the past three years we’ve worked on building a road map of not only how software should exchange and use the information that relates to each part but also monitor the outcome of new opportunities created. To that end, we’re happy to announce that we’ll be rolling out CRM support in 2016. The support and product needed will be included to all our customers for use with their commerce metrics and insights to better optimize campaigns. Customers who have been early adopters and users in the past year have seen significant upticks in their overall return on investment from marketing. Returns have doubled and even tripled in some cases. Marketing automation is using additional data and essential insights in business to act as signals for pushing and pulling levers in your AdWords campaigns – primarily controlling bids and budgets to influence impression share, increasing the overall share of top performers while highlighting deficient areas of your accounts and campaigns within. This allows us to optimize by knowing where more human capital effort needs applied, which is why I insist that for the time being marketing is not and will not be fully automated soon, but it is coming. As machines become more self-aware of design, styles and what is providing a greater return on investment from certain industries, many of those practices and successes carry over into other companies. It’s competitive in advertising but generally speaking, once you’ve managed millions of dollars you get the general gist of what works well for one company, can typically be applied in some fashion for another. This is a universal concept, not to say it always works or applies exactly the same way for one business as the next but let’s face it – companies have been using templates in all sorts of design and marketing for decades. Yes some of the best have high production values and are certainly original, that remains important. Marketing automation will level the playing field for all businesses investing into advertising in 2016. DESIGNA is on the forefront of those discoveries because we’re willing to adapt quickly, implement changes from feedback and stay the course of bringing the best machine learning available; battle tested, tried and true.

Marketing automation results

The results are clear, our customers end up saving between 20% to 40% on advertising that is less influential and could otherwise go unnoticed. We also are seeing better trend to reach target ROAS in a shorter amount of time then traditional methods. The reason is Google’s innovation with data science as it relates to advertising is a massive part of their business interest. In short, Google wants to provide a better experience for its users and businesses want connected with customers looking for their goods and service. We can utilize clusters of data collected through a variety of automated and manual input to determine what drivers help assist and cause conversion or new customer acquisitions. Typically we see a clear path visitors take, usually involving multiple marketing networks and channels. This data is referred to as an attribution model and is essential for machine learning and marketing automation because 9/10 times it’s a combination of placements or advertising networks that need amplified in order to increase ROAS. We will be supporting new, advanced and automated bidding methods in the new year; while growing our existing study using new software and data resources to help businesses effectively increase returns. Out of thirty case studies so far, 27 have been successful. Three were startups who may not have been the best fir for the studies due to constrained marketing budgets; we kept them part of the study for posterity sake. Part of our research revealed that impression share is still very important and has an indirect correlation to rising or falling conversion rates. Because impression share is so important, it’s important you’re not spreading budgets too thin and therefore we suggest consulting a marketing analyst to determine if you have a proper budget for your marketing, geography and goals.

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