Fiind artificial intelligence blog

20 Quotes for Every Stage of the Buyer’s Journey to Help You Get Better Results

20 Quotes for Every Stage of the Buyer’s Journey to Help You Get Better Results

Blog, Marketing
Regardless of industry, today’s sales and marketing professionals share a linchpin role: they’re all travel guides - travel guides for the buyer’s journey that is! What is the buyer’s journey? The buyer’s journey is the safari your customer takes as they decide to do business with you. While the exact path varies a bit by industry, there are four key common stages: [caption id="attachment_1636" align="alignnone" width="1200"] Understanding the key stages of the buyer's journey[/caption] Why is it important for salespeople and marketers to understand the buyer’s journey? As guides, understanding each stage of the buyer’s journey can help sales and marketing professionals embrace more customer-centric strategies to better deliver both customer and business success. When you are deeply familiar with the ins and outs of the buyer's journey, you’re in…
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No response to your emails? Perhaps they land in junk. Here are some tips.

No response to your emails? Perhaps they land in junk. Here are some tips.

Blog, Marketing
There’s an important change in your service: Pricing, Terms and Offers. Your Marketing team launches email campaign to all your existing customers. But your customers don’t receive the timely communications. Perhaps the emails land in Junk folder. Now you have some unhappy customers. Maybe you are a marketer with responsibility for digital campaigns, especially email campaigns to new or existing customers, and just launched a new demand generation campaign. The click rates are unusually low, and you are wondering what happened. According to Return Path’s 2018 Deliverability Benchmark Report, 15-17% of emails are getting delivered to “Junk” folder even though recipients are looking for it, thanks to the great powers of anti-spam technologies. The % of emails delivered to junk can be as high as 90%. We had the same…
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The trouble with MQLs…

The trouble with MQLs…

Blog, Data Science
How many times have you had trouble in agreeing upon what is actually a marketing qualified lead (MQL)? Marketing produces content, generates leads, and more so leads that qualify the agreed upon job titles, company size, revenue, etc. Yet, you hear.. Sounds familiar? The issue is not just because the attributes of an MQL is continuously evolving, but because MQL has its own set of shortcomings – which you might come across especially when you apply MQLs in an ABM scenario. We prefer to look at accounts as MQAs (Marketing Qualified Account), as that would equip marketers with effective information on whether a particular account qualifies to get into the ABM cycle or not. The problem with MQL is it is focused on the contact, whereas MQA gives you a…
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Customer data – are you turning into intelligence?

Customer data – are you turning into intelligence?

Blog, Data Science
There is a lot of customer data collected these days. But, how much do we really know about our customers? The very fundamental aspect of marketing is to give customers a better experience and align with their interests in real time. However, a part of our customer understanding comes from what we think is valuable to our customers, average deal size, repeat purchases, etc. But usually, these insights are narrow and not in real time. Therefore, the key question is – how much of what we know is fact, and how much is fiction? Data and its reliability It is absolutely important for marketers of today to be data-driven, and organizations are beginning to make large investments in customer data platforms. While it is one thing to capture data, ingest…
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Data quality first, smart applications next

Data quality first, smart applications next

Blog, Data Science
From the times we have been offering data enrichment solutions, we have come across a lot of companies that do not have the data they precisely need in their CRM and marketing automation systems. It can be due to missing lead information, or data has become old and irrelevant over-time and many more reasons. As one would expect – wrong data leads to reaching out to a wrong audience, thereby low sales conversion ratio, etc. This leads to two huge problems: There is a significant cost incurred due to bad data ($8.8 mil per year, as per Gartner) It becomes very difficult to predict where your revenue is likely to come from Fix your data quality first While martech is focused on smarter applications, we need to build a process…
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Top 5 Use Cases for Data Enrichment

Top 5 Use Cases for Data Enrichment

Blog, Machine Learning
While today’s marketers have access to data in abundance, it is important to ensure the data is relevant and up-to-date. Typically, customer data comes from varied sources such as website forms, social media, email lists, and more. For example, in a typical B2B scenario, we receive the information about a customer or a prospect, which in most cases is partial data and sometimes invalid information. You might just have their email address and name, or at best, their organization name and a phone number in addition to it. We all come across fake lead information, every now and then. Therefore, as a marketer, this data is not enough.  You neither know if the prospect is a firmographic fit (meaning, they fall under your target industry, organization size, job title, revenue,…
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