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Recommendation Engines – The future of web marketing automation

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Marketing automation (MA) is set to grow. MA is a category of technology that allows companies to streamline, automate and measure marketing tasks and workflows, so they can increase operational efficiency and grow revenues faster.

But how do you determine the priority of the marketing tasks that need to be performed? Which ones are relevant for your industry and company?  How do you determine which activities are helping with relevancy marketing – the mantra these days when we are all dealing with information deluge.

The answer to this could possibly lie in web marketing recommendation engines.

Recommendation engines and machine learning aren’t all that new. Regression, decision trees, K-nearest neighbor (KNN), support vector machines (SVMs), neural networks and naïve Bayes are established methods with known constraints and defined uses. If the benefits of a recommendation engine has been known for long then why has creating a recommendation engine for web marketing not been done in the past? Also when we hear about companies attempting to do this, we must consider how successful are they going to be and why?

The following trends are causing the need for web marketing recommendation engines to be created:

a)      Proliferation of online marketing methods – Today, with the proliferation of Internet usage, powerful direct response marketing – by using a combination of words and images designed to grab attention and convert it into sales- is no longer exclusive to big companies. The trick is to understand what’s valuable to your target customers and what strategies and tactics to employ. Creating persuasive content (websites or landing pages) accessible via any device (desktop, smart-phones, or tablets), direct response marketing techniques (psychology and copywriting) in search engine marketing (Pay-Per-Click or SEO), social media, display advertising (banners & videos), or email marketing to obtain qualified leads, gain influence, and attract sales are some ways. Additionally platforms to do each of these activities are increasing by the day to the point that keeping up with what’s good and the efficacy of each is challenging to say the least.

 

b)      The cost of data has reduced – Large volumes of storable data are being generated these days. Some of this data is very unstructured and dirty, like comments on social networks. Some is clean like GPS location data.  Additionally, it has become popular for companies to offer Application Programming interfaces (APIs) to allow partners and independent developers to leverage their platforms to create a variety of applications that cater to customer’s needs. So not only is more data generated in more places, but it is also more available to people in more places.

 

c)       Cost of software has come down, dramatically. Enterprise software that is adopted by organizations of significant scale and size runs into millions. The days of Enterprise Software – that cost an arm and a leg for licenses, and implementations that ran into multiples of what was paid for software is numbered.  Open source, dramatically low cost of hosting and scaling software has made the total cost of ownership of software to fall significantly.  This abstraction has allowed small teams to tackle big opportunities.

I would love to hear from you on whether or not you think this effort will be successful and why?

http://gitgrow.com


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