The Big Picture Math

How simple math shows IBM will get back to growth in the near future

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Oct 20, 2017
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IBM (IBM, Financial) reported a great third quarter, which took a lot of analysts and investors by surprise. The stock price shot up almost 10% following the earnings release. It’s IBM’s 22nd straight quarter of shrinking sales. But now suddenly everyone starts to believe that IBM will turn the corner soon.

I’ll skip the details of the quarter in terms of how great IBM did. You can read the press release and earnings call transcripts. In investing, sometimes simple math can tell a whole lot about the story and that’s the case with IBM – granted if you’ve done enough work to understand the business and to get comfortable with the assumptions behind the simple math.

Again, let’s not worry about the technicalities of the strategic imperatives and IBM’s legacy businesses for now. Let’s just look at how the math has worked so far. You can find all the relevant data and information from IBM’s public disclosures. You may have to read the transcripts and presentations to find some numbers, but it’s all out there.

Let’s go back to 2009, a year in which IBM recorded revenue of $95.8 billion. In 2009, the legacy business had not shown signs of troubles and the strategic imperative businesses had not become priorities for IBM. In that year, according to my calculation, the legacy business accounted for about 89% of the business while the strategic imperative businesses accounted for only 11% of the revenue base. Over the next two years, by 2011 when Warren Buffett (Trades, Portfolio) started to buy IBM and eventually accumulated a large holding, IBM’s revenue had grown to $107 billion, of which about 14% was strategic imperatives and 86% was legacy business, according to my calculation.

Then the trouble started to emerge as the legacy business started to decline and continued to do so until today. From 2012 to 2014, IBM’s legacy revenue declined by 8%, 9% and 12% while the strategic imperative businesses had grown impressively by 20%, 19% and 19%. By the end of 2014, legacy business accounted for 73% of the business, and the strategic imperatives accounted for 27% of the business.

In 2015, 2016 and 2017, the trajectory has been very consistent – double-digit growth in the strategic imperative business and high single-digit to low double-digit legacy business decline. If we do the math in the beginning of 2015, we’ll see that somewhere between 2017 and 2018, IBM’s revenue will grow again depending on how fast the strategic imperative business grows and how fast the legacy business declines. But the math is very straightforward – by 2018 or early 2019, strategic imperatives will account for more than 50% of the revenue base.

But IBM doesn’t need the strategic imperatives to be over 50% of the revenue to grow the top line again. If IBM’s strategic imperative business revenue accounts for say 42% of the total and grows 12%, and the legacy business say declines 7%, IBM will show revenue growth in this scenario, which is not too far from where IBM is today.

As illustrated above, IBM essentially went through an engineered multiyear transformation – in the early phase, the growth in the new business wasn’t enough to offset the decline in the legacy business; in the later stage, the growth in the new business will more than offset the decline in the legacy business. For investors who know enough about the business and have patience, IBM was a great time arbitrage opportunity. Wall Street analysts, though, have demonstrated their ever-growing short-term focus – instead of asking questions such as how exactly does IBM’s blockchain service help Nestle (XSWX:NESN) with food safety or how Watson competes against competitive AI offerings from other tech companies, almost all the questions on the earnings call are about near-term growth, margins and guidance.

Conclusion

One of the most important mental models is big picture math. In a world where short-term noise is becoming more and more prevalent and pernicious, it’s important to stay focused on the most important things and figure out how the big picture math works.

Disclosure: Long IBM.