There Are Only Three Ways to Grow — How Many Are You Missing? - SiteProNews
September 23, 2015
Image courtesy of (Ambro) / FreeDigitalPhotos.net
Remember the Three Stooges, Moe, Larry and Curly?
Moe was the dominant manipulator, Larry the pacifist follower, and Curly the ‘Divine Fool.’ While each character was vastly different, this extremely successful comedy act would not have been possible without all three personality types.
Successful Business Growth Also Works In Threes
There are only three ways to grow your business:
Assuming you are focused on what you do best, you are passionate about it, and it has an economic engine to sustain you (this, by the way, is another trio from the Jim Collins Hedgehog concept), does your company take advantage of all three paths to growth?
Traditionally, most companies spend the majority of their resources (time, budget, and energy) trying to gain more and more prospects.
In fact, most marketing efforts are focused solely in that direction.
Tactics like search engine optimization (SEO), search engine marketing (pay-per-click advertising, like Google AdWords), targeted direct mail, or social media all seem skewed toward simply getting new prospects.
I’m not sure why this tends to be the case. I’m sure there are many factors that contribute to such a common practice. One thing I do know for certain, however, is that only using this approach leaves value on the table and ignores critical opportunities for growth.
What About The Other Two?
Increasing your conversion rate of prospects into buying customers is usually perceived as a sales activity — not a marketing one. But in reality, it’s both.
It is no secret that there has been a great paradigm shift in the marketing world from traditional “outbound” marketing to “inbound” marketing.
Without getting into the details of the characteristics of each, what this means to companies is that buyers now have the upper hand in the marketplace over sellers.
It has been estimated that as much as 60 percent of the buying process today is completed prior to a prospect engaging with you. Within the next five years, this number could climb as high as 80 percent.
As a result, your marketing-for-growth-strategy must include elements of engaging, educating, and convincing your prospects prior to first contact.
It should go without saying that you also need to thoroughly train your sales staff so they become better and better about identifying the “right” opportunities and bringing those opportunities into your fold.
Finally, it costs an organization five times as much to get a new customer as it does to keep on old one, so why not get the most out of the relationships you already have with your existing customers?
Ask yourself — are my existing customers taking full advantage of everything we can do for them?
For example — if you provide a product, do you offer an accompanying service that your customers are aware of and use?
If you mainly provide a service but also sell products, are they buying your products?
Are there other products or services that you offer which existing customers are not buying from you but should? Use your sales team to dig deep with existing customers, then create marketing programs directed toward the existing customer to educate them on everything you can do for them.
And don’t forget about two other often-missed opportunities in digging deep:
Bringing back lost customers.
For referrals, establish formal programs that may incentivize your customers to refer you to their peers and professional network.
I also recommend training your sales staff on how to best ask for referrals — the process can be awkward if it is forced, but highly beneficial if done professionally.
For bringing back lost customers, the first step is identifying why they left in the first place. Once the corrections are made, consider creating a marketing campaign centered around these improvements to bring them back.
Vince Kostelnik is a Google certified professional and oversees all of ’ pay-per-click and Google AdWords efforts. He has seen AdWords generate an overflow of qualified leads for companies when done right. He loves managing and tweaking campaigns so that his clients are getting most for the PPC Dollar.
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Nice tip. Someone told me to try Nuxe oil for that look. Haven’t tried it. VCO works well too but you got to put up with the scent.
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Assignment Writin... (1)SPSS FAQ: How can I explain a three-way interaction in ANOVA?
How can I explain a three-way interaction in ANOVA?
If you are not familiar with three-way interactions in ANOVA, please see our
general FAQ on .& In short, a three-way
interaction means that
there is a two-way interaction that varies across levels of a third variable.& Say, for
example, that a b*c interaction differs across various levels of factor a.&
One way of analyzing the three-way interaction is through the use of tests of simple main-effects, e.g.,
the effect of one variable (or set of variables) across the levels of another variable.&
Next, we need to obtain the tests of the simple main-effects for each
level of a.& For this example, the residual mean-square is the error
term for all of the effects in the model and thus, for all of the tests of simple main-effects.&
There are at least three ways to conduct these tests.& Perhaps the
easiest way is to simply do some calculations by hand.& Another way to
do this is to use the lmatrix subcommand and specify the various
contrasts.& Once you understand how to code the contrasts on the
lmatrix subcommand, this is a simple method with minimal syntax
required.& An example illustrating this method can be found
.& A third way to do this is to use OMS (Output Management
System) to capture the necessary values and use aggregate to
calculate the necessary values.& The advantage of this method is that
you can get the adjusted p-values and critical F values per family error
however, this requires some SPSS syntax that some people find
intimidating.& An example illustrating this method can be found
We will use a small artificial dataset called
that has a statistically significant three-way interaction
to illustrate the process.& In our example data set, variables a, b and c
are categorical.& The techniques shown on this page can be generalized to
situations in which one or more variables are continuous, but the more
continuous variables that are involved in the interaction, the more complicated
things get.
We need to select a two-way interaction to look at more closely.& For the
purposes of this example we will examine the b*c interaction.& We can use
the plot subcommand of the unianova command to graph the b*c interaction for each of the two
levels of a.& We use the emmeans subcommands to get the numeric
values that are displayed on the graphs.
unianova y by a b c
/plot = profile(c*b*a)
/emmeans=tables(a*b*c)
/design = a b c a*b a*c b*c a*b*c.
We believe from looking at the two graphs above that the three-way interaction
is significant because there appears to be a &strong& two-way interaction at a =
1 and no interaction at a = 2. Now, we just have to show it statistically using
tests of simple main-effects.In SPSS, we need to conduct the tests of simple
main-effects in two parts.& First, we begin by running the ANOVA for both
levels of a.& This is easily done by sorting the data file on a, then
splitting the file by a, running the ANOVA, and finally turning off the split file.& To save
space, we show only some of the output from the unianova command.&
sort cases by a.
split file by a.
unianova y by b c
/design = b c b*c.
split file off.
Next, we need to obtain the tests of the simple main-effects for each
level of a.& For this example, the residual mean-square is the error
term for all of the effects in the model and thus, for all of the tests of simple main-effects.&
Let's rerun the model from above (with fewer subcommands so as to reduce the
amount of output).
unianova y by a b c
/design = a b c a*b a*c b*c a*b*c.
As we can see, the sums of squares error is 16 with 12
the mean square error is 1.333.
Now, let's look at the b*c interaction at a=1.& The test statistic is
obtained by dividing the mean square of the b*c interaction from the ANOVA with
just b and c at a=1 by the mean square error from the original ANOVA.& To
do this, we need sort the data file by a, split the data file by a, and then run
the ANOVA with b, c and the b*c interaction as predictors of y.
sort cases by a.
split file by a.
unianova y by b c.
The mean square of the b*c interaction is 20.333.& So we have
20.333/1.333 = 15.25.& To test the b*c interaction at a=2, we have
.250/1.333 = .1875.& The first test, F(2, 12) = 15.25
Clearly, the first F-ratio, F(2, 12) = 15.25, is much larger than the
second, F(2, 12) = .1875, but how can we tell which are statistically
significant?& There are at least four different methods of determining the
critical value of tests of simple main-effects.& There is a method related to
Dunn's multiple comparisons, a method attributed to Marascuilo and Levin, a
method called the simultaneous test procedure (very conservative and related to
the Scheff& post-hoc test) and a per family error rate method.
We will demonstrate the per family error rate method but you should look up
the other methods in a good ANOVA book, like Kirk (1995), to decide which
approach is best for your situation.& We divide our alpha level, 0.05, by 2
because we are doing two tests of simple main-effects, so our new value of alpha
is .025.& The idf function requires us to provide 1 - alpha, so we have 1 -
.025 = .975.&
compute p1 = idf.f(.975, 2, 12).
As& you can see, p1 is approximately 5.10.& This indicates that the
b*c interaction is statistically significant at a=1 but not at a=2.
In an ideal world we would be done now, but since we live in the &real&
world, there is still more to do because we now need to try to understand the
significant two-way interaction at a = 1; first for b = 1 and then for b = 2.
For a = 1 and b = 1:
compute filter1 = 0.
if b=1 and a = 1 filter1 = 1.
filter by filter1.
unianova y by c.
filter off.
We will construct this F test in the same way we did above, namely using the
mean square for the effect, in this case, the effect of c, divided by the mean
square error from the original ANOVA.& As we can see from the output above,
the mean square for c is .32.& Hence, we have 32/1.33 = 24.& We can
obtain the critical F-value using the idf function.
compute p1 = idf.f(.975, 2, 12).
&The critical value is approximately 5.1, so our F(2, 12) = 24 is
statistically significant.
For a = 1 and b = 2:
compute filter2 = 0.
if b=2 and a = 1 filter2 = 1.
filter by filter2.
unianova y by c.
filter off.
We will construct this F test in the same way we did above, namely using the
mean square for the effect, in this case, the effect of c, divided by the mean
square error from the original ANOVA.& As we can see from the output above,
the mean square for c is .667.& Hence, we have .667/1.33 = .5.& We can
obtain the critical F-value using the idf function.
compute p2 = idf.f(.975, 2, 12).
&The critical value is approximately 5.1, so our F(2, 12) = .5 is not
statistically significant.
Only the test of simple main-effects of c at b = 1 was significant.& But we're not done yet,
since there are three levels of c, we don't know where this significant effect lies.& We
need to test the pairwise comparisons among the three means.& We will do this using the Sidak correction for multiple tests.
filter by filter1.
unianova y by c
/emmeans=tables(c) compare(c) adj(sidak) .
filter off.
As shown above, only one of the comparisons is
statistically significant.& However, the Sidak correction can be
conservative.& If we had used a different correction, say the Tukey HSD,
all three comparisons would be statistically significant.& We should note
that the error term used in these comparisons is not the error term from the
original three-way ANOVA.& We would need to use syntax similar to that
shown above to save that error term to a new data set and then use it in the
comparisons (as shown on
).& We might want
to use the error term from the original three-way ANOVA because we are going
post-hoc tests of that analysis.Hopefully, we now have a much better
understanding of the three-way a*b*c interaction.&
Please note that the process of investigating the three-way interactions would have be similar if we had chosen a different two-way interaction back
at the beginning.
Summary of Steps
1) Run full model with three-way interaction.
& & 1a) Capture SS and df residual.
2) Run two-way interaction at each level of third variable.
& & 2a) Capture SS and df for interactions.
& & 2b) Compute F-ratios for tests of simple main-effects.
3) Run one-way model at each level of second variable.
& & 3a) Capture SS and df for main effects.
& & 3b) Compute F-ratios for tests of simple main-effects.
4) Run pairwise or other post-hoc comparisons if necessary.
References
Kirk, Roger E. (1995) Experimental Design: Procedures for the Behavioral Sciences,
Third Edition. Monterey, California: Brooks/Cole Publishing.
The content of this web site should not be construed as an endorsement
of any particular web site, book, or software product by the
University of California.From Wikipedia, the free encyclopedia
In , a three-way comparison takes two values A and B belonging to a type with a
and determines whether A & B, A = B, or A & B in a single operation, in accordance with the mathematical .
Many processors have
that support such an operation on primitive types. Some machines have signed
based on a sign-and-magnitude or one's complement representation (see ), both of which allow a differentiated positive and negative . This does not violate trichotomy as long as a consistent total order is adopted: either -0 = +0 or -0 & +0 is valid. Common
types, however, have an exception to trichotomy: there is a special value "NaN" () such that x & NaN, x & NaN, and x = NaN are all false for all floating-point values x (including NaN itself).
In , the functions strcmp and memcmp perform a three-way comparison between strings and memory buffers, respectively. They return a negative number when the first argument is
smaller than the second, zero when the arguments are equal, and a positive number otherwise. This convention of returning the "sign of the difference" is extended to arbitrary comparison functions by the standard sorting function , which takes a comparison function
and requires it to abide by it.
(for numeric comparisons only),
(since version 7), , and , the spaceship operator &=& returns the values -1, 0, or 1 depending on whether A & B, A = B, or A & B, respectively. In Python 2.x (removed in 3.x), the cmp function computes the same thing. In , the compare function computes the same thing. In the
standard library, the three-way comparison function compare is defined for all types in the Ord ; it returns type Ordering, whose values are LT (less than), EQ (equal), and GT (greater than):
data Ordering = LT | EQ | GT
Many object-oriented languages have a three-way comparison , which performs a three-way comparison between the object and another given object. For example, in , any class that implements the Comparable interface has a
method which returns a negative integer, zero, or a positive integer. Similarly, in the , any class that implements the IComparable interface has such a
Since Java version 1.5, the same can be computed using the Math.signum static method if the difference can be known without computational problems such as
mentioned below. Many computer languages allow the definition of functions so a compare(A,B) could be devised appropriately, but the question is whether or not its internal definition can employ some sort of three-way syntax or else must fall back on repeated tests.
When implementing a three-way comparison where a three-way comparison operator or method is not already available, it is common to combine two comparisons, such as A = B and A & B, or A & B and A & B. In principle, a compiler might deduce that these two expressions could be replaced by only one comparison followed by multiple tests of the result, but mention of this optimisation is not to be found in texts on the subject.
In some cases, three-way comparison can be simulated by subtracting A and B and examining the sign of the result, exploiting special instructions for examining the sign of a number. However, this requires the type of A and B to have a well-defined difference. Fixed-width signed integers may overflow when they are subtracted, floating-point numbers have the value NaN with undefined sign, and character strings have no difference function corresponding to their total order. At the machine level, overflow is typically tracked and can be used to determine order after subtraction, but this information is not usually available to higher-level languages.
In one case of a three-way
provided by the programming language, 's now-deprecated three-way
statement considers the sign of an arithmetic expression and offers three labels to jump to according to the sign of the result:
IF (expression) negative,zero,positive
The common library function
and related languages is a three-way lexicographic c however, these languages lack a general three-way comparison of other data types.
Three-way comparisons have the property of being easy to compose and build
comparisons of non-primitive data types, unlike two-way comparisons.
Here is a composition example in Perl.
sub compare($$) {
my ($a, $b) = @_;
return $a-&{unit} cmp $b-&{unit}
|| $a-&{rank} &=& $b-&{rank}
|| $a-&{name} cmp $b-&{name};
Note that cmp, in Perl, is for strings, since &=& is for numbers. Two-way equivalents tend to be less compact but not necessarily less legible. The above takes advantage of
of the || operator, and the fact that 0 is considered false in Perl. As a result, if the first comparison is equal (thus evaluates to 0), it will "fall through" to the second comparison, and so on, until it finds one that is non-zero, or until it reaches the end.
In some languages, including , , , etc., comparison of lists are done lexicographically, which means that it is possible to build a chain of comparisons like the above example by putting the values into lists
for example, in Ruby:
[a.unit, a.rank, a.name] &=& [b.unit, b.rank, b.name]
The three-way comparison operator for numbers is spelled &=& in , , ,
and , and is called the spaceship operator because it reminded
of the spaceship in an
game. Another coder has suggested that it was so named because it looked similar to Darth Vader's TIE fighter in the
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