What was I thinking? Spend $1500 on wearable technology that is not quite ready? The same desire that made me get the first Apple 1, the first PC, the first iPhone, Android (Gphone back then) and many other firsts. When you want to understand where technology is going, you can’t live it from afar you must immerse yourself.
There are 2 parts of the Glass experience: (1) How others react to you and (2) what you experience
How others react
This is funny, top questions
- Are you recording me now?
- Did you take my picture?
- How do they work?
- Can I try them on?
- How do I get a pair
- Can you see through my clothes?
From the questions, I could tell people are a bit confused. There has been some negative press. “Glassholes” wearing Glass into a bar and acting inappropriate. These are probably the same people who you would not want to be around anyway. I think this was a mistake by Google. The first set of people that got Glass were people that liked gadgets and had money to afford it. They should have made the selection more stringent; people who actually wanted to build something on the Glass.
My Glass Experience
After the first day, I had buyers remorse. After the second day I was on the fence. By day three, I was seeing all sorts of new applications that could be built on the Glass platform. Fun.
Today, I drive, email, record videos, take pictures, attend conferences and generally have fun with Glass. The battery life is short. I always travel with the charging cable.
Today is the day that Google is selling Glass to everyone (I was on an 18 month waiting list). It will be interesting to see how wider adoption will impact the platform, public acceptance and applications available.
As an experiment, the next person that asks me if I can see through their clothes with Glass, I am going to do my best to say “yes” with a straight face.
Trade shows. You have 10 seconds maximum to engage and get the interest of a passer by. Time is critical. Time is everything.
Eliminating filler words such as “Um”, “Ah”, “Er” and “You know” is paramount. It kills your presentation and will cost you the sale.
So you’ve been is sales for years and you think it’s ok?
I’ve got news for you: When I hear constant “Um, ah, er, eh, you know” in conversation you are stamped as irrelevant. You are an amatuer. You’ve had some great sales months, but you are not a great sale rep. Language and the articulation thereof is the engine that drives sales. If your communication ability sucks time from my life, I just don’t have time for you. I am not alone.
I am being honest with you, right now. You may be right out of college or have a few sales years under your belt. Maybe you just never made the effort to improve. You may think it is ok; your friends may talk this way and reinforce this habit.
If you are thinking this way, you are wrong. You will never be great in sales without mastering communication.
The first step to fixing the language filler problem is realizing you have one. If you have the desire, this video will help. Good luck.
What and when to automate and when to intervene is one of the most far reaching decisions you will make on the journey to a clean CRM. In fact, this automation vs. intervention decision quandary will impact all processes in your business. Instead of an in-depth how-to-clean your CRM tutorial, I thought I’d share some simple axioms that I base my decisions on when bringing efficiency and automation to a process.
#1 Don’t confuse automation with efficiency
Efficiency is how fast and how cheap a process can be done. Automation is applying non-human processes into a system. It is a subtle difference and that is why people get confused. For example: lead assignment can be automated, but if it is being done poorly or incorrect, it is not efficient. This is a natural lead in to #2.
#2. Never automate an unsuccessful process.
People can make mistakes, but to really screw up you need a computer. Make sure your processes work correctly, regardless of how fast. Once you have your process down, then apply automation.
#3. Automate a single process at a time.
There are exceptions and sometimes you can’t avoid doing a few things at once. The reason for this is immutably tied to #4.
#4. Measure what you automate.
Define what success is so that you can recognize it when it happens. When successful, automate something else and measure again.
#5 Complex systems are constantly redesigned
No one that I know can design a complex CRM system that stays 100% to the original design. Why do major software implementations fail and go over budget? Simple, the initial design did not encompass the complexities of the real world. Balance design with diving in and checking your premises. Be agile, be creative and get user feedback at critical milestones.
Duduping a CRM is like an equation waiting to be solved. Just like a high school math, the more variables you know the values for, the easier the problem is to solve and the more likely you will come up with the correct answer.
CRMs include is a vast number of “Data Markers”. These markers are a literal road map to filling in missing data. For example, if 100% of the emails for a particular company have the email format of Firstname.Lastname@domain.com, then you can probably fill in missing emails for other contacts with confidence. If you have the email domains for contacts, but the account record is lacking a website, that can be filled in too.
A company’s website address is a unique identifier. It is more important than the company name. Look at Peoplesoft as an example: long after the company was acquired by Oracle, you could navigate to www.peoplesoft.com. A few years later, it was totally absorbed…but the website did outlive the company.
Done properly, leveraging data markers within a CRM allows the properly trained consultant to pre-fill data for a more complete picture…BEFORE deduping. In addition the data should be standardized (sometimes called Normalized) before the dedupe process is done.
If you take the correct measures of: (1) data normalization and (2) data fill before deduping, your dedupe process will be greatly improved.
Donato Diorio @idonato and Michael Farrington @michaelforce help dispel myths surrounding data quality.