How can you measure the true path to conversion? Unless you have proper attribution, it is difficult to know which content converted prospects and push them over the edge to become consumers.
Attribution means that you must know how to track, measure, and allocate value. Without an understanding of the data, you cannot understand how each of your marketing channels contribute to your bottom line.
5 Benefits of Attribution Models
- Minimize conversion time
- Optimize media plans
- Eliminate wasted impressions
- Yield greater ad-spend returns
- Allocate resources
An excellent example of attribution noted in a post by Matthieu Betton, Head of Global Car and Strategic Accounts at Sojern, highlights when the “last click fails to capture a traveller’s journey as he moves towards a booking.
It doesn’t take into account the multiplicity of interactions and influences (both brand-led and outside of brand’s control) that lead to the traveller’s eventual conversion – and there could be as many as 18.2 touch points on average, according to Google, before a consumer makes a final purchase decision.”
Betton notes, “another downside of last click is that it equates clickers and bookers, lumping both into the same category when research shows that clickers are not always bookers and vice-versa. So, if you’re running a display campaign, consider other measurements, such as view-through conversions.” Betton describes that view-through conversion occurs when a person is impressed by an ad, does not click, but searches or visits the website later and converts.
Algorithmic Attribution Methodology
The algorithmic technique uses statistics for dynamic analysis of digital data touch points. Conversion tracking is the most common example.
Rules-Based Attribution Methodology
The rules-based methodology is much simpler to implement than algorithmic attribution. For example, touch-point weightings that determine how credit for sales and conversions is assigned in conversion paths.
If you’re just starting to sort out your attribution, begin with the rules-based approach. Betton shares three easy steps to start your rules-based attribution:
- Get all your data in the same place. Set clear KPIs that are aligned with business objectives.
- Then begin to dig deeper: based on the nature of your business, define individual touch-point weightings or use available models (i.e. Google analytics multi-touch). Now you can begin the thought process around attributing value to each action prior to conversion. For example the likes of Airbnb will require a very different attribution model to Hilton despite both being in the hospitality sector.
- Now you can test, optimize, measure success and repeat the process.
source: Matthieu Betton, Head of Global Car and Strategic Accounts, Sojern.
You may have heard that Big Data has become the big thing that is propelling companies large and small. Thanks to the constant innovation in technology, the collection and storage of vast amounts customer information has never been easier than it is right now and it’s only going to get quicker and simpler.
Also, the world of tools analysts can utilize to make sense of this info is growing at a rapid pace. Vicky Byrom, Senior Analytical Consultant at Aquila Insight, shares her perspective on the issues facing analyst.
There are now mountains and mountains of new data being produced everyday from a variety of interesting, worthy and valuable sources. Alongside this there’s the avalanche of shiny digital toys with which to slice, dice and interpret all this information. However, this leads to the big concern about data quality.
There have always been problems with data integrity. But now there’s just a whole lot more data to clean up before we can get to the insight. The 2015 Experian Data Quality benchmark suggests that, on average, US companies believe 32% of their data is inaccurate. That’s right, there’s just shy of a third of all data produced which we should be approaching with caution.
This poor data impacts how effective we can be as analysts. We have to spend more time ‘cleaning’, making approximations or just simply ruling out data sources. If customer data is captured inconsistently or incorrectly, analytical insights can be misleading.
In auto-adapting models, the risk is even more significant as the computer brain, unless instructed to do so, may not see how unreliable the captured information is and could direct customers down the wrong contact strategies. It comes back to the age old saying, “Garbage in = Garbage Out”.
So where is it going wrong? There are some data integrity issues that are too big to fix, inherent from legacy systems. We’ve all encountered the aging, patched up systems that have had work around after work around just added to make sure business as usual can continue.
These systems require large scale overhauls, and major investment in data governance. Fixing them is going to be expensive, time consuming and will need buy in from the guys at the top or it’s just never going to happen.
But there are areas where we can improve and influence change. For starters we can advocate, enthuse and generally shout about the third party solutions that are out there. Ready made packages that can go over the top of those unwieldy existing legacy systems.
They cost a fraction of the price in both time and money compared to fixing what is there or building from scratch and will eliminate that errant one third of the data. We should also be looking to make improvements in the processes where data is captured manually, like in most call centers. As analysts we should have the courage of our convictions, step up and champion the promotion of better, cleaner and more reliable data whenever we can.
The Cost of Poor Data Quality
It often feels like we analysts are the only ones who see just how much potential value is lost from poor and unusable data. It is frustrating when we know how it could benefit businesses in terms of targeting the people we should be talking to, at what price and when we should be doing it.
We need to start talking to many more departments in our organizations to emphasize the real value of data capture. We need everybody that touches the data, directly or indirectly, to realize that it is a monetizable asset and that it’s worth is devalued if it’s not captured correctly.
We need to show stakeholders how they can help us understand and improve the information we capture, get them involved in shaping the analytics to turn data into insight that benefits the whole business and our customers.
The upside of all of this is that with better, more reliable data, analysts can spend more time building new, exciting and most importantly, meaningful models for stakeholders, which becomes a win for everyone.
source: Vicky Byrom, Senior Analytical Consultant, Aquila Insight.
The age of real-time analytics and social media, makes true customer engagement easier to measure but harder to achieve. A solid content marketing strategy is even more important to engage customers. The average consumer has become an adept multi-tasker, capable of flitting between multiple devices and services at the same time.
Combine this with the growing use of ad blocking technology and despite today’s hyper-connected digital landscape it’s become much harder for brands to reach consumers through a combination of traditional marketing and digital advertising.
Fortunately, there’s light at the end of the tunnel in the form of data-driven content marketing – a new approach that’s capable of cutting through the oversaturated media environment to reach target audiences with a message that’s both interesting and relevant to them on an individual level.
The role of social discovery in social media
Social media is painted as a challenge to marketing today due to its attention-stealing nature, but it’s the very concept of social sharing that brands should be taking advantage of. Brands are investing heavily (both time and money) into digital channels like Twitter, Facebook, and Instagram, but they’re not taking advantage of the core value proposition these networks offer – social discovery.
From a content marketing perspective, the use of social discovery is a powerful yet non-invasive way to target potential new customers while also maintaining interaction with existing customers that are already engaged.
With more content now available online than ever before it’s become crucial for brands to be present where the customer is, and where conversations are taking place, in order to take advantage of this trend and integrate social discovery into their overall marketing approach. By doing so, they can introduce new products in a far less aggressive and more engaging way.
Social discovery and the future of content marketing
It’s clear that social discovery has an important role to play in the future of content marketing, but for brands to truly stand out they need to introduce a data-led approach to their efforts. Where a data-led approach differs to traditional content marketing efforts is in creating a new way of interacting with consumers on a personal level. Creating content based on what’s most likely to be shared over social media will generate the biggest buzz and greatest number of interactions.
By applying data-driven insights to content marketing, it’s not only possible to accurately predict which pieces of marketing collateral will have the greatest success, but also to A/B test variations of the same content to ensure the biggest possible reach is achieved.
Trialing and testing content before releasing it will also help brands gain a reputation for generating content people seek out and share, rather than block. With this in mind, all brands should use data to inform their content marketing approach; to personalize their efforts for different demographics in ways that haven’t previously been possible, and to target consumers based on their individual preferences.
The value of great content
For audiences, great content is still great content. Video, text, or images that drive passionate conversations and fierce loyalty, even in today’s oversaturated digital world, are far more valuable than those that do not. Introducing data-led content to a marketing strategy opens up the opportunity for brands to get closer to their target audience than ever before. By focusing efforts on creating newsworthy, sharable, and actionable content that’s underpinned by data, it’s possible to dramatically increase consumer engagement.
source: Juliette Otterburn-Hall, Chief Content Officer, Beamly
Solutions to Common A/B Testing Mistakes
When was the last time you evaluated your marketing strategy? If you have not done it for awhile, now is the time to revisit your online marketing campaign and assess the impact of your A/B testing. You might have overlooked the common mistakes of over represented product information and an old approach of dripping market promotion. Also, check out the video below on A/B testing mistakes.
KISSmetrics explains the common A/B testing mistakes that people make and provides a few useful solutions:
Mistake One: Over representation of your product
A common mistake in marketing strategy is over representation of the product by exploiting fake testimonials. Do not exaggerate product information or product value.
The policy of honesty remains true and applicable in launching and promoting products. If you continue to bombard your clients with false or misleading information, you will be more likely to increase negative customer reviews and drive bad word of mouth.
Solution One: Enhance the value of the product to deliver value that meets your customer’s needs.
Stay focused on creating a compelling hook or promise that truly and properly represents your products. Direct your customers to landing pages that include specific information that addresses their wants, needs, and expectations.
So the question lies with how you would gauge which optimization tool best suits your customers’ needs? The solution is to simply ask your customers. Asking the right questions paves way to understanding their main concerns.
Survey tools like Qualarro, can be helpful in uncovering customer needs. Also, customer development calls, keyword inbound traffic, and heat maps are some tools that if effectively used can help you understand more about your customers.
By gathering survey data, it will become more clear how to create marketing campaigns that will most likely trigger positive responses from customers. As a result of these combined efforts — knowing the needs of your customers and enhancing the overall value of the product — your clients will continue to love and buy your products.
Mistake Two: Making customers wait too long to experience the value of the product.
Customers have no extra time to discover your products, so it is important that you help them know how to experience your product. Once customers love your product, they will use it regularly and pay for it. Also, you will likely find that customers are willing to give feedback and provide comments to make your product better.
Solution Two: Make them experience the value of the product.
Once you let clients know the true value of your product, and if the product is aligned with what has been promised, then you will be successful at earning their trust. In return, customers will be happy to share testimonials and recommend your product through social media and word-of-mouth.
Knowing how to avoid common A/B testing mistakes and applying the above solutions will greatly improve your marketing strategy and business profit. Have you been successful in eliminating some A/B testing mistakes in your business? Let other people read it from your post!
Personalization is without a doubt one of the buzz words with the most equity behind it at the moment when it comes to digital marketing.
Regardless of the sector, brand owners are fiercely competing to find the most effective ways to grab and sustain their customers’ attention.
When people talk about adopting a more personalized approach to how they use email marketing, it appears the results can vary quite considerably.
Here are five top tips to improve personalization on your email campaigns.
1. Personalization is more than a name
The days of relying on sending emails solely personalized by name are gone. The ability to directly address an individual may appear personal at face value, but it is inherently lazy. Just because you received an email saying “Hi John”, doesn’t mean the rest of the message beneath it has any degree of relevance to you.
In fact, most emails probably don’t, and as result they make the recipient feel disconnected and disengaged. Equally, if the content inside the email is not relevant to the addressee, the fact it opens addressing you directly by name is irrelevant.
Adopting a higher level of personalization may sound like a scary or expensive prospect but it shouldn’t. It may sound obvious but listening is incredibly powerful. What have your customers told you? How are they interacting with you?
Every interaction generates data, which breeds insight that can be used to improve the content you send. Ensuring that you don’t ignore what your customers are saying about you can add extra value to your email and generate a far more positive response.
2. Don’t obsess about the customer preference center
The preference center used to be the definitive source for information on customer preferences. However in 2016 its value has severely diminished. It is such a simple form of data capture and doesn’t come anywhere close to addressing the ever changing needs of every individual in your customer base.
While it does provide a snapshot of an individual’s preferences at one given moment it relies on the recipient updating it in order for it hold any long-term value. Brands therefore need to ensure they use this data as a base level and identify other data sources across the business which add additional insight, ideally in real-time. That way messages can be more precise and adapted to customers changing needs.
3. Invest in a
DMP) lets you collect and analyze data about your customers-including behavioral, geographic and profile data-from every touch point in one platform. The key benefit they provide is that they give brands an integrated picture of all its data sources, from both its own first party data and third party sources.
Moreover, a DMP provides brands with the ability to identify its most valuable target audiences and with it improve performance. As a result it makes it far easier to personalize communication materials on an individual basis and on a unified view of how they user actually interacts with the brand. This enables brands to massively improve the relevance of their content and choose more predictive business rules to decide on the best content to show to the user.
4. Use the power of emotion
Standing out in a crowded inbox can be difficult. Even if a brand has recipients who regularly open its emails there is never any guarantee they always will. They might easily miss it, or feel that not every message they get is fully applicable to their needs. Brands therefore should look at ways they can appeal to their recipients’ emotions with enticing subject lines and hero messages.
Emails which say “you’re in our top 5%”, acknowledge recent interactions, or play on other vanity statements are not only a personal reflection of how that user has interacted with the brand, it is typically followed by an incentive for further interaction.
Understanding what powers of persuasion will best influence customers should therefore be a key requirement of the data capture and analytical process.
5. Don’t underestimate the power of timing
It is important to remember that email is only a small cog in a much bigger and rapidly growing digital machine. The days are long gone where marketers can think and act in terms of siloed communication channel strategies.
Unpin your thinking from only using email for a nurture campaign or regular face time with your customers. Use it for different and surprising reasons, be bold, and test and learn alongside the broader digital communication approaches.
source: Mark Ash is Managing Director Teradata Interactive International
A new study from Juniper Research found that the transaction value of online, mobile and contactless payments will reach $3.6 trillion globally this year, up from $3 trillion last year.
The research also forecasts that mobile wallet adoption would continue to accelerate in developing markets. So, the question becomes for startups: How are you planning to leverage this growth with new product or service offerings?
Why The Increase In Contactless Payments?
The research attributes the increase to soaring contactless payments continuing as well as improved infrastructure and increased card payment limits in key markets. Different countries have different spending limits, for example, In the US the limit is $25 and in the UK the initial limit was £20 for a contactless payment, this has since been increased to £30.
The research also suggests while cards will account for 90% of contactless payments over the next 5 years, Samsung and Apple will help drive consumer awareness and usage of smartphones to fulfil transactions. In fact during Apple’s keynote event, Apple exec Greg Joswiak revealed that over three million cards were registered in China for Apple Pay within three days of its launch in the country. The newly announced Apple SE also has Apple Pay built in, being the cheapest iPhone available it will make the technology accessible to even more people.
It’s not just Samsung and Apple driving digital payments. Facebook, Pinterest and Instagram have already announced ‘buy’ buttons allowing users to purchase products from their apps within a few clicks. Further to this real-time messaging apps are also moving into commerce. Facebook has already announced users will be able to make purchases through its messenger. WeChat in China saw more than 32 billion ‘red envelope’ monetary gifts sent in a 6 day period in February.
source: Tobias Matthews, Fourth Source