Successful cross selling is driven by knowing what to sell and to whom. Greyferret solidifies this idea by implementing smart, customer behavior based cross sell programs designed to improve online customers' average order values. Watch the video to learn more on how Greyferret can help breeze through complicated Cross Sell challenges.
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What good is getting customers, if you're unable to retain them? In an era of eroding loyalties and predatory competition retaining your online customers has become a science. Knowing when and how to interact with customers is a good start in theory, but in practice, well easier said than done. Watch the video on how Greyferret helps the modern ecommerce marketer succeed in customer retention.
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Retention is like the middle child of a marketing family. Perennially dwarfed by its siblings, Acquisition and Churn, you don’t pay much attention to it. Until things go belly up of course and then you’re left scrambling to salvage customer relationships beyond repair.
We all know the numbers involved in retention. But let’s focus on some real world actions that can help hold on to customers in the big bad world of online commerce.
Discount Ladder
Customer behavioral data is the holy grail of online commerce and every eCommerce marketer is aware of the inherent benefits of driving behavior based customer campaigns. But ever wonder how this works in practice? How can a marketer use customers’ purchase and email engagement behavior to create and drive customer relevant marketing programs? Check out this video on how Greyferret translates raw customer data into marketing actions to learn more.
In our last post I introduced to you the SPIRAL Framework that can help marketers plan their technology roadmap. But a framework is directional in nature and the nitty-gritties of the individual phases are critical for ensuring outcomes. Commencing this week, we shall look at each of the SPIRAL framework’s individual components in greater detail.
One of my more interesting conversations as a Marketing Technology Consultant was with a Fortune 500 Marketing division some time ago. This probably opened my eyes to the more pragmatic challenges facing marketers than any other knowledge source and personally gave me some insights into how some of these challenges could be addressed. The participants in the conversation were the Global and Regional Digital Marketing heads and the internal IT divisions of the customer organization. The conversation ran something like this.
If you run an ecommerce store or connected to its money making even in the remotest way, then you know that your customers are your lifeblood. Now keeping your customers engaged and happy with your site are never-ending challenges. You’re perennially at risk from competitors, constantly changing product lines and of course, the highly fickle nature of customers who are ready to abandon your store at the drop of a hat (or cart in this case).
Meet Jim. Jim is looking to throw the ultimate birthday party for his 4 year old son and has quite a ‘to- do’ list for the party. Jim decides to kick off his party prep by logging on to his trusty ecommerce store and begins shopping, beginning with ingredients for the birthday cake. He spends a few minutes filling his cart with essentials like flour, eggs, milk, sugar, frosting and candles. Done with his purchase, Jim is on the verge of the checkout.
Association analysis – in the context of consumer product affinity, is also called Market Basket Analysis. It is an unsupervised algorithm – easier to comprehend if you understand the supervised model. Consider a case where the temperature and humidity in a particular location are given, you are trying to predict the quantity of rain on that day. The inputs are standardized and you observe / record the inputs i.e. temperature and humidity and of course, the rainfall.
The goal of classification is to build rules or models and represent them in a simple readable form using past decisions. There are multiple techniques that can accomplish this task of gleaning intelligence from existing data – Neural Networks, SVMs, Decision Trees and so on. We’ll focus on Decision Trees in this article since they sit well with the Marketing use cases like predicting conversion rates, buying decisions etc. Sounds obtuse? We’ll demystify this in a moment.
Clustering is the task of grouping a set of objects so that objects in the same group are more similar to each other than objects in another group. Well, now that the formal definition is through, we can get down to brass tacks – just replace the term objects in the definition with “customers “ and we’ll be on our way.
The Data Model per se, is a critical element in performance management not just for marketing platforms but any enterprise application that is data driven. No Surprise, that this is possibly the least focused on area on Marketing platforms. A detailed analysis of Marketing db structures is really out of scope here, but let it suffice to say that some MSPs have standard data models that they force fit for all marketers, some have a degree of flexibility while some are 100% customizable.
Marketers need to comprehend that a cloud based marketing platform is enticing but SHARED across other customers of the ESP / MSP. If their campaigns have a performance problem, you can bet your boots that your holiday / week end campaign gets hit as well.
The challenge of course, is in getting this context – MSPs may not share this information and degree of “Infrastructure Sharedness” too freely. After all, as a marketer, you subscribe to SLAs around the infrastructure not to a “labeled” marketing server itself.
We looked at the business impacts of the build process and how every marketer needs to worry about it. Here’s a technical dimension to managing the campaign build process effectively, though as a solution it is challenging to implement unless you own the marketing platform lock, stock and barrel.
Solution: Move the Build & Merge process outside the operational Email Marketing database.
Benefits:
The Campaign Build Process has multiple business implications for Marketers even though technically the process is managed by an ESP. Think of all the Black Friday or Cyber Monday campaigns that struggled through the door and the number of times you have kept your fingers crossed hoping to get the Easter campaign out on Easter. Some of the critical implications of the Build process include but are not limited to
Time to Customer:
Marketing technologists need to be increasingly aware of the constraints that come in her/his away in staging successful email campaign. The delivery process hogs database resources and renders the system unavailable and / or too slow for other contenders like online subscriptions, data loads and reporting. Marketers need to manage the delivery process effectively as the key to ensure predictable campaign delivery, high availability to competing processes and in case of SaaS based platforms – direct Customer Satisfaction.
Before getting onboard a PhD in Data Mining or acquiring one, there is some level of magic a marketer can do with the behavioral database he has set up, without the need for advanced analytical Tools. These are quick and dirty methods but can boost conversions, reduce blast volume and in general power up your marketing efforts with much needed “customer centric” intelligence. Some basic knowledge of SQL can help though it is not mandatory.
With 2015 galloping forward, we have cumulated what we believe to be the most pertinent predictions for CMOs this year. These predictions have been curated from the most trusted minds across industries, viz. Forrester, IDC, marketingtechblog.com and marketingland.com, amongst others.
Prediction 1: CMOs Will Cement, Not Just Declare, Their Position on the C-Suite
There are three internal sources of behavioral data that marketers can use to drive engagement. The interplay between these varied sources and a combinatorial approach can drive interesting and highly practical engagement strategies. Most marketers have ready access to the following sources of transactional information. These sources of data can be aggregated and used in isolation or together to handle live situations and drive positive customer engagement.
Having got through the data wiring aspects of connecting real time transactions into the repository, it is tempting for Marketers to put in a Customer Value indicator in place – something like the RFM model. This can give tangible quick wins like
We all Know Why: Given the need to focus on relevant, timely messaging to customers, it has become imperative for marketers to have access to real time customer data. The data drill down to “timely & relevant” are multi-faceted behaviours translating to different sources– Purchase, search, browse and abandonment from ecommerce, returns and logistics from ERP, Engagement or Campaign Stats from ESPS and Reviews & “Customer Speak” from Social & Speak Out portals.
The deliverables of today’s CMO have been reconstructed to demonstrate a dramatic shift towards more measurable outcomes; from brand positioning and product/service awareness towards a more quantifiable contribution towards the organization’s bottom line.
“It is not about playing differently – it is about a different game.” (R. Bernhard & A. Olderog, Vivaldi Partners Group, 2014) The last three years have witnessed a revolutionary shift in the role of the CMO; one that has compelled us all to sit up, take notice, and make changes. From once being a purely transactional role, confined to organizational and product positioning, branding, and pricing, the role of today’s CMO is far more collaborative, calculated, and customer-centric.
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