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Mobile apps help to attract new customers, increase engagement and drive conversions, but this often requires the user to keep coming back.
Some in-app offerings will be enough to keep the user returning, but other times the users might need a little reminder to send them back to the mobile apps they may not have opened in a while.
According to data from Localytics, 22% of people who download an app only use it once. This means that marketers really need to be thinking about how they can attract their customers back to their mobile apps.
This thinking should go beyond just app downloads and focus more on value and engagement. This can be done in a number of ways, in terms of marketing, these can include push notifications, location-based services, in-app messaging and SMS.
If you run an ecommerce site, you probably use email to announce sales, engage customers and drive repeat purchases.
But now that the vast majority of your customers use smartphones, you can follow the lead of most large ecommerce sites which are using SMS just like email to drive repeat visits and purchases.
If you collect mobile phone numbers and have permission to text them, include links in your SMS back to your site (also known as Smart SMS) and grow sales through one of the most direct and engaging marketing channels available.
This blog post isn't to convince you of the value of SMS for driving ecommerce sales, most smart businesses are doing it already. Our goal is to answer a key question: how do you measure the effectiveness of SMS and track the sales from each campaign?
Advertising on the internet and mobile has increased by 17.5% to £3.04bn in the first half of 2013 according to the IAB, an increase of £607m compared to 2011.
Analytics has played a key role in this growth by helping marketers accurately measure return-on-investment (ROI) and justify reallocating traditional media budget to digital marketing. However, with the amount of data now available to digital marketers via analytics, they’re in danger of becoming data squirrels that hoard data but do nothing with it.
There aren’t enough analysts in the world or hours in the day to manually analyse all the available data, and crucially, turn it into actions which optimise revenue outcomes.
If you follow marketing and digital related blogs and news sites, then it won’t come as a surprise to you that marketing professionals and CMOs are expected to show how activity achieves business results.
As marketing professionals, we need to have an in-depth and real time understanding of how we contribute to our organizations’ or clients’ revenue. In addition, we must also be able to have an ongoing conversation with our sales teams that is revenue focused, far away from “the feeling” that our campaigns are generating good results and increasing brand awareness.
As spending on digital takes a greater share of the overall marketing budget, greater scrutiny is being brought to bear upon the CMO to justify these investments. Attributing revenue to a specific channel or combination of channels, rather than just attaching it to the “last click” is a challenge that is beyond the capabilities of most.
A good approach to doing this is to create your own marketing dashboard.
First off, what is it?
Well don’t let anyone tell you it’s down to sample size, or about measuring everything. It’s about combining datasets (sometimes ‘dirty’ ones), contrasting them in different ways, and doing it as quick as possible.
Sometimes this necessitates great computing power, but not always. You can read more about such technology as Hadoop and GreenPlum in this nice little article).
Datasets are multiplying as we measure lots more than we used to. This means our thinking has to broaden – no longer is ‘what can we do with our database of email addresses?’ the question, rather ‘what data can we look at to give us the best idea possible of a customer’s stage in the buying cycle and what they’ll be receptive to next?’
The definition of big data isn’t really important and one can get hung up on it. Much better to look at ‘new’ uses of data.
So, here’s some examples of new and possibly ‘big’ data use both online and off-.
Epagogix is a company that analyses scripts in an attempt to forecast US box office performance or TV audience size.
On the back of this script analysis, the company recommends box-office-increasing enhancements.
Nick Meaney of Epagogix was speaking to the audience at PUNCH, the Festival of Marketing’s creative-focused event in the East end of London.
At the end of September, Google confirmed the roll-out of Secure Sockets Layer (SSL) encrypted search to all users.
In short, this means that keyword-level data for organic (non-paid) Google traffic will no longer be provided. Consequently, website owners will no longer be able to view the keywords a visitor used in Google to find their website.
This announcement from Google will have a huge impact on the industry, with search marketers around the world rethinking metrics to track SEO performance.
The average website might convert only around 2% of leads, with rates much lower in some industries.
Even though the conversion rates are still such a low percentage of visitors, internet marketers are often still looking at data sets from 100% of visitors when evaluating web performance.
In other words, because 98% of visitors didn’t show themselves to be serious customers or convert, arguably 98% of the data they’re looking at is skewed and possibly incorrect.
I've started rounding up notable posts each month, with aim of ensuring our dear readers never miss a useful article, or a blog post that can make you feel a bit more of a jedi.
Here's the roundup from September, with 10 posts for you to bone up on SEO, analytics and the like, and three posts to sit back and enjoy with coffee.
In my last post, Three reasons why Big Data is a big load of baloney, I threw a stone down the streets of London and New York and wound up hitting a few Big Data advocates.
And it is abundantly clear that Big Data arouses passion in people (or at least, as much passion as one can humanly feel for data.)
The thing about Big Data is that it can be interpreted in many ways. In this post, I take a step back and look at how Big Data is affecting digital marketing as a whole, and how maybe, just maybe, it’s not a cliché but a fundamental shift in how we do business.
As promised, here’s part two in my series. Is Big Data all it’s cracked up to be? Read on to find out more.
Over the last couple of years, Big Data has been unavoidable. It’s not just big, it’s massive. If you throw a stone down the streets of London or New York, you’ve got as much a chance of hitting a big data guru as you do a social media guru.
Undoubtedly, there is great power in data, but is Big Data all it’s cracked up to be?
50% of my brain thinks Big Data is great, and 50% of me thinks it’s a neologism. I’ve found it difficult to reconcile all of the varying information out there about it.
So join me for the first part of a two-part series looking at Big Data. In part one, I’ll look at Three reasons why Big Data is a big load of baloney. And next week in part two, I’ll look at Three reasons why Big Data is awesome.
The marketer’s dream of getting the right message to the right person at the right time is now not only a reality, but for many the right time has become ‘right now’.
In the third post on real time customer intelligence we examine four steps to delight customers in the ’live contact zone’.