2017 Loyalty Trends

Cogensia is moving in the right direction by taking full advantage of the 2017 Loyalty Trends with our new CMP technology platform.

To meet consumer needs across the growing number of touch points: social, mobile, e-commerce, in-store and SMS brands must focus on generating a single customer view.

In 2017, brands will be challenged with developing true customer loyalty through personalization and relevancy.

It’s a mobile first world today. Consumers will look for functional apps that add value to their lives. Socially, brands will push past expected social media channels of Facebook, Twitter and LinkedIn to grow.

Technology is the great enabler for marketers. Data Services are King!

Is Your Marketing CRM Ready for Big Data?

Big Data is transforming operations across the spectrum of industries and functions, including logistics, manufacturing, accounting, and even legal services.  One key area often identified is marketing, specifically data-driven marketing.  By tracking and analyzing customer interactions, the theory goes, a marketer can provide a higher level of relevance in marketing, including social media, email, real-time messaging, and mobile apps.  While Big Data is described in many ways (unstructured, real-time, etc.), the key for marketers is to make sure there is a plan and tools to utilize Big Data to meet marketing objectives.  For these purposes, Big Data will be described broadly, in terms of customer interactions regardless of channel.  There are four factors that brands must assess to get the most of out their Big Data for CRM:

  1. Can you get access to the data you need?  Do your email / mobile / web / social agency and providers have methods of accessing engagement and interaction data?  This data, in its structured or unstructured form, is where the “real time” action is.  The raw data needs to be linked and actioned from each channel and across channels.  If you do not have the tools to ingest this data, you will not be successful in using “Big Data”, and your first step is to find partners and tools that give you this access
  2. Do you know who your customers are?  It’s one thing to know that someone liked your Facebook post, but it’s something deeper to know that she is a loyal customer.  Having the ability to connect the dots is important – it’s what brings Big Data into customer relationships.  A website visitor being a customer you haven’t seen for a while in your stores? Great opportunity to communicate that you miss them right then and there!
  3. Do you know what you would say?  Is your marketing team equipped to generate personalized, customized messaging across multiple channels based on the combination of factors (transactions, demos, type of interactions, etc.) for a customer?  For instance, which interactions might generate an action in a different channel?  Browsing for a product on your website may trigger a DM piece or an email for the product, for example.  To execute at this level, an organization must have a culture of testing, and the ability to mix channels in customer communications.
  4. Can you execute?  Are your channel partners able to manage 500 triggers flowing through their systems?  Can your email provider execute dynamic content?  This is critical to executing personalized messaging at scale, which is where the power of Big Data comes to life.  A system that can observe an interaction (purchase, email click, website visit, etc.), identify who the individual is, define the right messaging, and execute, sometimes in real time, is fully impacting customer relationships with Big Data. If you are looking at how Big Data fits into your marketing organization, these four areas will be important to evaluate, to see how ready you are.  This also allows you to plan resources, suppliers, and marketing expenditures wisely where they will have the biggest impact.

7 contrasts IT Executives need to know before making investments

 

Technology has upended industries, capabilities, and functions, and has the ability to automate a great number of tasks. The evolution of technology has meant the automation of more sophisticated tasks, including things like resume screening for HR, legal reviews of documents, and automated marketing communications.

Internal IT organizations are tasked with optimizing internal and external processes, vendors, and software to optimize the return on technology investment. This works well in operational and staff capabilities where processes are streamlined, and the inputs and outputs are well understood.

Marketing organizations, however, are a different breed. Marketers’ needs change; their ability to adapt to competitors, build loyalty, and test new campaigns and offers, across all digital and non-digital media. This causes their technology needs to be more complex. Methodologies, understanding of fickle customer interests and behaviors, and changing channels (Facebook rules, SEO changes) mean that the routine process of installing sophisticated software and tools and then moving on to the next initiative doesn’t apply.

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About a week ago I was meeting with some of our analytic staff discussing differentiated strategies for distinct markets. It was one of those rare, fun conversations that extends beyond tactical needs of a project and really allows you to dig into strategy and (gasp) academic theory behind a methodology.

During the course of the conversation, the word ‘analytics’ was tossed around as it so often is. After what I considered to be enough patience, I finally interrupted the conversation to make a point. So many people use the term analytics for such a broad range of activities that it has functionally become meaningless. I have heard the term used as a description of the most basic reporting function; I have heard it used to describe the aspirational, strategic goals of some of the smartest marketers I have met. I wanted to point out to my young Padawans the impotence of throwing that word around, especially in the presence of an ‘analytic’ audience.

The response I received surprised me:  I was asked, “So what does analytics mean (to you)?” I am sure many of you have been in a similar situation, where you have adopted a position (and probably it was well founded or researched) and had your talking points, but you haven’t really exhaustively thought about that position for quite some time. I didn’t have a rote answer or elevator speech to describe the essence of analytics. Sure, it is generically used to describe anything that touches data, and sure it is often used in a way that minimizes what it is that I do for a living, but when asked directly how I define it, I didn’t have a rehearsed response.

 

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