If it's a fact that automation is indispensible in addressing the data sets of big data, we also know that marketing personalization is crucial. Can we hope for personalized automation?
Task automation saves time while allowing us to prospect a larger number of marketing contacts. The number of customers is then ultimately higher. This approach is all the more tempting in the era of big data. The data collected is immense. It's as much material to feed the sales pipeline.
Basing your approach on a large volume is tempting, as long as the quality follows suit. The logic of quantity has its limits. There are many commercial approaches. Their overburdened recipients are bored. Mass marketing is gone, today it's about refining the target, personalizing the message. How to prospect broadly when refining?
This is where machine learning can really be appreciated. On the data sets of big data, manual processing is not even an option - firstly because it would be as energy-sapping as time-consuming. It would also, however, be weak and not very appropriate. Automatic data processing is very much welcomed, especially if it's intelligent. Machine learning is a form of artificial intelligence. The machine integrates the data but also their approximations and their interactions to gain a continued analysis. This massive processing results in an autonomous and instant analysis. The historic wealth of the panel gives the tool a relevant predictive ability. This is a case of a learning algorithm.
We can seek to predict different user experience (UX) scenarios manually. This consists of envisioning all of the specific cases and predicting associated actions. But experience shows that humans tend to idealize the consumer path. It rarely occurs as expected. The "machine" brings real added value, because it envisages all of the specific cases in an exhaustive and impartial way. A computer program would be simultaneously more thorough and more appropriate.
Rolling out richer scenarios allows you to implement an inbound marketing strategy. It consists of getting prospective customers to come to you, creating an inbound call or click. This is in contrast to the outbound strategy where you go towards your prospective customers via an outgoing call or email. The interest of inbound strategies is in the low implementation costs in regards to the results it allows you to obtain. Keeping a blog, starting a newsletter, social media presence and even the provision of a white paper are channels for lead generation - commercial contacts. Their contents invite others to discover your brand and the associated expertise. We refer to this as content strategy, or content marketing.
The aim is to direct your content in a contextualized manner within your scenarios. By succeeding to offer the right content at the right moment, the likelihood of a lead becoming a customer is at 80%. To do this, the challenge is to regularly supply your leads - lead nurturing - and to look out for their signs of maturity. A prospective customer going onto your site and downloading your white paper is a positive sign: is this the right moment to send them a specific email further detailing your offer? These actions, ingrained throughout your scenarios, follow a lead-scoring logic.
New generation CRM (Customer Relationship Management) software are based on a technology which links big data with machine learning. Result: intelligent automation marketing. That's the case with Plezi, which collects all of the information related to your prospective clients in order to create customized scenarios for them. The application of leads thus manages the inbound managing chain from A to Z, in an automatic and personalized way. Connected to your website, the solution will, for example, decrypt the most recent visits of one of your contacts, the articles which they read. A qualitative basis on which you can personalize your newsletter: selecting the articles they haven't read, for example, or the themes likely to interest them.
Plezi respects the confidentiality of databases and the personal information of its users. Nonetheless, the content wealth that passes through the solution allows the publisher to establish cross-sectional analyses. Restored in the form of contextualized data, this information gives user companies a benchmark against which to rate their performance in relation to their sector. The application also constitutes a source for suggestions on operational order matters, such as: when to send the content? At what pace? In accordance with the success rates of actions carried out by each company, Plezi is able to optimize the impact of the marketing strategies of each company.
Marketing automation is an innovative solution in personalizing your marketing strategy, while approaching a large sample of prospects. CRMs like Plezi offer personalized lines of automation to make this technology profitable in the framework of simultaneously innovative and inexpensive commercial prospecting